Medicare Pricing

io.github.pipeworx-io由 GitHub 账号 pipeworx-io 发布v0.2.1更新于 Nov 3, 2026

Medicare支付什么——医师费用表、实验室费率和每日单位限额。

AI 翻译由 AI 模型根据下方的原文描述翻译，可能有误。

原文描述

What does Medicare PAY — physician fee schedule, lab rates, and per-day unit limits.

已验证Streamable HTTP可网页运行金融数据与分析

概览

Medicare Pricing 的托管 MCP 服务（gateway.pipeworx.io）；无需登录；Streamable HTTP；56 个工具。

暂无 AI 概览：以下信息来自注册中心条目和服务本身。

端点
gateway.pipeworx.io
最近检查

安装

在 SourceWeft 中

  1. 打开 控制台中的 Medicare Pricing，将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.pipeworx-io%2Fmedicare-pricing/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "medicare-pricing": {
      "type": "http",
      "url": "https://gateway.pipeworx.io/medicare-pricing/mcp"
    }
  }
}

README

medicare-pricing

What Medicare pays — physician fee schedule, lab rates, and per-day unit limits. The companion to medicare-coverage, which answers whether something is covered but not what it is worth.

Part of Pipeworx — an MCP gateway connecting AI agents to 1865+ live data sources. This is an independent, unofficial integration — not affiliated with, endorsed by, or published by the upstream provider.

Tools

ToolAnswers
medicare_physician_payment"How much does Medicare pay for 99213 in Los Angeles?" — RVUs and dollars, locality-adjusted.
medicare_lab_rate"What does a comprehensive metabolic panel reimburse?" — national CLFS rate.
medicare_lab_test_lookup"What does Medicare pay for Signatera, and which MolDX LCD applies?" — brand name → PLA code, current CLFS rate, and the MolDX LCDs/articles for the test's category.
medicare_dmepos_rate"How much does Medicare pay for a CPAP machine (E0601) in Texas?" — state-priced, rural vs non-rural.
medicare_opps_rate"What does Medicare pay a hospital outpatient department for CPT 33289 (CardioMEMS implant)?" — status indicator, APC, relative weight, national payment rate, copay, device/drug pass-through status.
medicare_code_units_limit"How many units of this code will they pay for in one day?" — NCCI MUE.
check_code_pair"Can I bill 00142 and 64474 together?" — NCCI PTP edit + modifier indicator for a code pair.
explain_ncci_edit"What is bundled into (or bundles into) this code?" — every recorded PTP edit for one code.
medicare_fqhc_pps_rate"What is the FQHC PPS base rate for 2026?" — CMS's national Federally Qualified Health Center per-visit base rate, by year.
medicare_pricing_coverageWhich vintages are loaded, including the PTP baseline vintage.

Auth

None. No key, no account.

Data sources

All from CMS, all downloadable files with no API:

  • Physician Fee Schedule relative value files (RVUs + GPCIs) — annual
  • Clinical Laboratory Fee Schedule — quarterly
  • DMEPOS fee schedule (durable medical equipment, prosthetics, orthotics, supplies) — quarterly (as necessary): A=Jan, B=Apr, C=Jul, D=Oct
  • OPPS Addendum B (Hospital Outpatient Prospective Payment System payment rates by HCPCS code) — quarterly: Q1=Jan, Q2=Apr, Q3=Jul, Q4=Oct
  • NCCI Medically Unlikely Edits — quarterly
  • NCCI PTP quarterly additions/deletions/revisions files — quarterly (see below — a narrower source than the other three)
  • FQHC Prospective Payment System base payment rate — annual (see below — a hand-maintained table, not a downloaded file)

FQHC PPS base payment rate — a hand-maintained table, and why

medicare_fqhc_pps_rate is different from the rest of this pack: it is not backed by Supabase or a downloaded bulk file, because there is neither. CMS publishes the FQHC PPS national base rate exactly once a year, as a single new dollar figure inside a CMS Manual System "Recurring Update Notification" Change Request PDF — one number, one PDF, once a year. Standing up an ingest pipeline for that is not proportionate, so the pack carries a small table of {year, base_rate_usd, effective_date, market_basket_update_pct, prior_year_rate_usd, transmittal, change_request, publication} in src/index.ts (FQHC_PPS_BASE_RATES), five rows (CY2022–CY2026), each read directly off its own primary CMS transmittal.

This is the national BASE rate only — before the per-FQHC Geographic Adjustment Factor (GAF) CMS applies on top. The GAF-adjusted amount for a locality is medicare_fqhc_gaf (below).

Geographic adjustment: medicare_gpci_lookup and medicare_fqhc_gaf (fleet #3398)

medicare_gpci_lookup returns the work / practice-expense / malpractice GPCIs for a locality or a whole state with no procedure code (years 2024-2026 loaded; 2024 and 2025 were backfilled with scripts/ingest-medicare-pricing.mjs --gpci-only). medicare_fqhc_gaf returns CMS's own published FQHC GAF per locality for CY2018-2026 (each year from CMS's own GAF publication for that year; 2018 uses CMS's February 2018 updated table, 2021 and 2020 the updated releases) and the GAF-adjusted per-visit rate (2022-2026; null for 2018-2021, whose national base rate is not carried). The GAF table is CMS's published number, not derived here, and is a small generated source file (src/fqhc-gaf.ts, 109-112 localities per year; ~50 KB). Refresh each January: download the new year's FQHC GAF file from CMS and run node scripts/build-fqhc-gaf-table.mjs 2018=<file> ... 2026=<file> 2027=<file> (ascending; the script finds columns by header name because CMS's layout changes yearly).

Refresh: CMS issues the following year's transmittal in Nov/Dec (the CY2026 update was dated 2025-12-05 for a Jan 1, 2026 effective date). Add a row to FQHC_PPS_BASE_RATES each year once it posts — search CMS's own transmittals (Pub 100-04 Medicare Claims Processing, or Pub 100-02 Medicare Benefit Policy) for "FQHC PPS Calendar Year".

ADLT list — brand name to billing code

medicare_lab_test_lookup exists because callers name a test by its brand ("Signatera"), not its code ("0340U"), and nothing else in the catalog maps one to the other (fleet #2426).

Source, and why it is the only one. CMS's list of Advanced Diagnostic Laboratory Tests (https://www.cms.gov/files/document/advanced-diagnostic-laboratory-tests-under-medicare-clfs.pdf) gives code, lab, proprietary test name, approval date and initial-period payment for 18 tests. It is a US federal work (17 USC 105). The sources that were checked and rejected:

  • AMA PLA long-descriptor PDF — "CPT® Copyright 2026 American Medical Association. All rights reserved." It also lists only the codes changed in the latest cycles (143 of them), and 0340U is not among them.
  • Palmetto MolDX DEX registry — needs a registered account, even for the "public user" catalog, and hides Z-codes from public users. That's an auth wall.
  • CMS Coverage API article code tables — need a caller's CMS/AMA licence token (already how medicare-coverage handles it).
  • NCBI GTR — knows Signatera and Guardant360 but its cptcode field is empty for them.

That made coverage ADLT-only, and fleet #2458 measured the cost: Oncotype DX (81519, a Category I MAAA code, not an ADLT) routed here 5/5 and came back found: false. Resolution now runs through three sources in order:

  1. CMS ADLT list (src/adlt.ts, below).
  2. MAAA brand table (src/brand-codes.ts) -- non-ADLT branded tests whose proprietary name is tied to a code in CPT Appendix O and which each have a brand-named or category MolDX billing article on the CMS MCD (Oncotype DX breast/DCIS/colon/prostate, Prosigna, MammaPrint, EndoPredict, Breast Cancer Index, Cologuard, Prolaris, Decipher, Afirma, ConfirmMDx, 4Kscore). Only brand -> code -> lab FACTS are kept, never AMA descriptor text. Every code was checked to carry a CLFS rate on 2026-09-26. To add a row: confirm the pair in a public source, confirm medicare_lab_rate has a rate, and set coverage_topic to a phrase from a live MolDX article title.
  3. NCBI GTR, live -- where the performing lab registered a CPT code (e.g. Afirma -> 81546, Decipher -> 81542). Labelled lab-reported. Sparse: Oncotype DX and Signatera carry no code there, which is why it is last.

A test found in none returns found: false, reason: brand_not_found with sources_checked naming all three, and a pointer to medicare_lab_rate.

Copy, not proxy. The list is a two-page PDF with no API, and the gateway has no PDF parser, so the rows are baked into src/adlt.ts. The PDF's "Test Descriptor" column is AMA PLA text and is not copied. Every call sends a HEAD to the PDF and compares Last-Modified with the snapshot's. If CMS has republished, the response carries snapshot_current: false. To refresh: download the PDF, extract its text (pypdf works), update the rows in src/adlt.ts, and bump ADLT_DATA_AS_OF and SNAPSHOT_LAST_MODIFIED.

Coverage. Each test has a coverage_topic, a phrase that is ours and not CMS's. It matches the title of the MolDX LCD/article family for the test's category (for Signatera, "Minimal Residual Disease"). The tool reads the live, keyless CMS Coverage API reports and returns the active LCDs and billing articles with that title. Retired ones come back only with include_retired: true. This matters because contractors re-issue MolDX policies under new numbers: Noridian's L38816 and A58456 were retired 2026-02-05 and replaced by L38814 and A58454. Whether a specific test is listed is answered by the billing article's code table, not by the title match.

Vintage is not a detail

Fee schedules change annually and NCCI/CLFS quarterly. Quoting a 2026 procedure at 2024 rates is a wrong number with billing consequences, so every response states the year (and quarter where quarterly) it used, and sets year_was_defaulted when the caller did not name one. Asking for a year that is not loaded returns year_not_loaded rather than silently answering with a different year's rates.

Locality is not state

Medicare pays by geographic locality. California alone has nine. The same office visit (99213, 2026) pays:

LocalityNon-facility
Bakersfield, CA$99.52
Alabama$87.79

A 13% swing a state-shaped answer would hide. medicare_physician_payment resolves the locality explicitly, states what it resolved to, and lists other localities that matched so a caller can pick a different one.

Payment is computed with the real formula and the arithmetic is returned so it can be audited:

((work RVU × PW GPCI) + (PE RVU × PE GPCI) + (MP RVU × MP GPCI)) × conversion factor

Facility and non-facility are both given — a procedure pays less in a hospital because the facility bills its own fee separately.

Whether two codes can be billed together — the full PTP baseline

check_code_pair and explain_ncci_edit now cover the FULL NCCI procedure-to-procedure (PTP) edit baseline, not a rolling change window (fleet #1293, 2026-09-07). CMS distributes the complete PTP edit table behind an AMA CPT licence click-through (/license/ama?file=... on cms.gov). Bruce authorized accepting that licence on the company's behalf via the Needs Bruce dashboard (2026-09-07); the licence text was read in full before any data shipped — see "NCCI PTP licence" below for the quoted terms and the reasoning for why this pack's serving shape stays inside them.

Loaded: the 2026 Quarter 3 baseline (v322r0, effective 2026-07-01 — the newest vintage actually in effect; CMS had already posted Q4 2026 at ingest time, but that isn't effective until 2026-10-01 and using it would have answered today's queries with tomorrow's rules). The 8 baseline files (practitioner + hospital × 4 code-range splits each) hold 4,498,118 raw historical-episode rows — the same (column1, column2) pair can have several add/delete/re-add episodes over the years — collapsed to 4,109,749 distinct pairs by keeping each pair's open-ended (currently active) episode, or else its most recently closed one. The prior 2025q1..2026q3 quarterly change files are re-applied ON TOP of the baseline, in that order, so a pair the quarterly files recorded as deleted cannot be resurrected by an older baseline row.

A NOT-FOUND answer now means CMS has never recorded a PTP edit for that pair — not merely "no recorded change in this window", which was the scope limit before this fleet task. medicare_code_units_limit remains the separate units edit (one code, not a pair) and was never affected by this limit.

NCCI PTP licence

The CMS licence click-through (same boilerplate CMS uses across its whole AMA CPT-derived file family — the physician fee schedule, MUE, and PTP downloads all carry it) reads, in relevant part:

CPT codes, descriptions and other data only are copyright 1995-2025 American Medical Association. All rights reserved... You, your employees and agents are authorized to use CPT only as contained in the following authorized materials of Centers for Medicare and Medicaid Services (CMS) internally within your organization within the United States for the sole use by yourself, employees and agents. Use is limited to use in Medicare, Medicaid or other programs administered by CMS... Any use not authorized herein is prohibited, including... making copies of CPT for resale and/or license, transferring copies of CPT to any party not bound by this agreement, creating any modified or derivative work of CPT, or making any commercial use of CPT.

Read maximally literally, "internal use" and "CMS-administered programs only" would forbid nearly all third-party commercial reuse of ANY Medicare fee-schedule data — an interpretation the entire health-IT industry operates against daily (claims clearinghouses, EHR vendors, and billing software all reference CPT codes commercially). This pack's existing, already-shipped design applies the standard industry reading of the opening sentence — "CPT codes, descriptions and other data" are the copyrighted, licensed material; the CMS-computed derivative numbers indexed by those codes (RVUs, payment amounts, edit modifier indicators) are federal work product and are returned, while the AMA's own descriptive text is never stored or served. The PTP baseline's own "PTP Edit Rationale" column (a ~12-value CMS-authored categorical reason set, e.g. "Standards of medical/surgical practice") is not stored either, purely to keep this load a strict superset of the existing table shape (no schema change) — not because it reads as AMA content. This is the same theory already governing this pack's RVU/CLFS/MUE data; the PTP click-through does not introduce a new prohibition beyond what the rest of the pack already operates under, but the literal breadth of "internal use" / "CMS-administered programs" language is flagged here for anyone reviewing licence exposure across the pack.

No procedure descriptions — except DMEPOS

Every PFS/CLFS source file opens with "CPT codes, descriptions and other data only are copyright American Medical Association". The RVU file scopes it precisely — codes and descriptions only — so the CMS-computed numbers are federal data and are returned, while the AMA's descriptive text is neither stored nor served. Callers pass a code they already hold.

DMEPOS is the exception, on purpose. It uses HCPCS Level II codes (E/K/L/A-codes), which CMS itself maintains — unlike CPT (HCPCS Level I), which is AMA copyright. The DMEPOS source file's own DMEREAD/DMEBACK documentation carries no AMA/copyright notice anywhere (checked), so medicare_dmepos_rate DOES return the code description.

OPPS — what a hospital outpatient department is paid

medicare_opps_rate answers the same "what does Medicare pay" question for a hospital outpatient department claim, as opposed to a physician office (medicare_physician_payment) or a lab (medicare_lab_rate). Source: CMS, OPPS Addendum B, the quarterly national payment-rate file by HCPCS code. Measured size: 19,278 rows for the CY2026 October release — small, loaded whole, same shape as clfs_rates/dmepos_rates already in this pack.

Device and drug/biological pass-through is the status indicator, not a separate column. CMS's own "Drug and Device Pass-Through Expiration during Calendar Year" column in Addendum B is, in practice, not a usable date field — checked against the CY2026 October release, every non-blank value in that column was a stray change-marker rather than a date. The reliable signal is the status indicator itself: SI='H' is a pass-through device category (paid separately, outside the standard APC amount, for as long as the device holds that status — typically 2-3 years from market entry, after which CMS reprices it, often to packaged SI='N'), and SI='G' is a pass-through drug/biological. device_pass_through/drug_or_biological_pass_through on every response are derived from SI, not from the expiration column.

A packaged code (SI='N', among several other status indicators) returns a valid status_indicator and status_indicator_meaning but apc: null and no payment rate — that is a correct, normal answer: OPPS pays for that code only as part of whatever procedure it is billed alongside, never on its own. CardioMEMS (C2624, the implantable pulmonary-artery pressure sensor) is a worked example of exactly this: it has been on the market long enough that it is no longer pass-through (SI='N', packaged into the implant procedure 33289's payment), which is the expected, current state — not a missing-data gap.

No OPPS short descriptors — a mixed-code-type file

Addendum B's own header states "CPT codes and descriptions only are copyright ... American Medical Association", and the file mixes AMA- copyright CPT (HCPCS Level I, numeric codes like 33289) with CMS-owned HCPCS Level II codes (like C2624) under one shared descriptor column, with no per-row schema signal to tell which is which. Unlike medicare_dmepos_rate (Level II only, so the description is safely CMS-owned), medicare_opps_rate follows the stricter pfs_rvu convention instead: no descriptor is stored at all, for any code in this table. A caller passes a code they already hold.

DMEPOS pays by state, not locality

Medicare pays DMEPOS by state (not by the physician-fee-schedule locality), with a separate rural and non-rural amount per state. CMS's own source file is one row per (HCPCS, modifier) with 53 state/territory columns × 2 (non-rural/rural) = 106 rate columns; this pack keeps that row grain and folds the 106 columns into one JSONB map rather than exploding to ~374,000 rows per release. medicare_dmepos_rate always returns both figures, labelled, and says explicitly when a state carries no separately-adjusted rural amount (a real "no adjustment" fact from the source file, not a missing value) rather than presenting a bare null.

Refreshing

bash
node scripts/ingest-medicare-pricing.mjs                    # current vintages: RVU, GPCI, CLFS, MUEnode scripts/ingest-medicare-pricing.mjs --year 2025 --rvu rvu25d --clfs 25clabq4
node scripts/ingest-ncci-ptp.mjs                             # PTP quarterly change overlay (default: 2025q1..2026q3)node scripts/ingest-ncci-ptp.mjs --quarters 2026q4            # add just the newest quarter on a refresh
node scripts/ingest-ncci-ptp-baseline.mjs --dir /path/to/8-zips  # full PTP baseline re-load (rare — new CMS vintage only)                                                                  # ALWAYS re-run ingest-ncci-ptp.mjs immediately after —                                                                  # the quarterly overlay must land on top of the baseline,                                                                  # not the other way around (see script header comment).                                                                  # Requires manually downloading the 8 baseline zips                                                                  # through the CMS AMA-licence click-through first — no                                                                  # automated fetch can complete that flow (503s to curl                                                                  # and to a bare browser GET of the file URL; only a real                                                                  # interactive click on the license page's Accept button                                                                  # works).
node scripts/ingest-dmepos.mjs                                # newest DMEPOS release (default: 2026 release C)node scripts/ingest-dmepos.mjs --year 2026 --release D         # add a new release when CMS posts one
node scripts/ingest-opps.mjs                                   # newest OPPS Addendum B release (default: 2026 October)node scripts/ingest-opps.mjs --year 2026 --month january       # add a new quarterly release when CMS posts one

Loaded as of writing: 19,226 RVU rows, 109 localities, 2,081 lab rates, 3,531 DMEPOS code/modifier rows (2026 release C, 53 states/territories each), 19,278 OPPS Addendum B rows (CY2026 October / Q4), 33,354 MUEs, 4,109,749 distinct NCCI PTP pairs — the FULL CMS baseline (2026q3, v322r0) with the 2025q1..2026q3 quarterly change files layered on top (fleet #1293, 2026-09-07; superseded the prior 182,286-pair change-window-only coverage). All loaders refuse rather than reporting an empty parse as success.

ingest-opps.mjs resolves the current download from the OPPS Addendum B release landing page, same pattern as ingest-dmepos.mjs. The landing page's own link routes through CMS's /apps/ama/license.asp?file=... AMA licence click-through page — but, unlike the NCCI PTP baseline elsewhere in this pack, the underlying file is NOT actually gated: fetching the plain https://www.cms.gov/files/zip/<name>.zip URL directly (bypassing license.asp entirely) returns the zip with no click-through, no cookie, and no Referer needed — verified live, 2026-10-21. The script extracts that direct URL from the landing page and fetches it, rather than going through license.asp at all.

ingest-dmepos.mjs resolves the current download from the DMEPOS release subpage rather than guessing the zip filename — CMS's DMEPOS naming is not stable (a release page 404s before it is posted, and old release URLs have 301-redirected to renamed paths), so it reads .../dmepos-fee-schedule/dme<yy>[-<letter>] for the current .zip href on every run, the same pattern ingest-medicare-pricing.mjs already uses for the PFS RVU page.

ingest-ncci-ptp.mjs is additive/idempotent by design — re-running the full default window is safe (upserts on service_type,column1,column2), and adding a new quarter to --quarters widens the window forward without needing to re-fetch history.

Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):

json
{  "mcpServers": {    "medicare-pricing": {      "url": "https://gateway.pipeworx.io/medicare-pricing/mcp"    }  }}

What this endpoint actually serves

tools/list at https://gateway.pipeworx.io/medicare-pricing/mcp returns the tools in the table above plus the shared Pipeworx meta-tools — ask_pipeworx, discover_tools, search_within, remember/recall and the rest of the gateway-wide set. So the tool count you see is larger than this table: a single-pack endpoint currently lists roughly 30 shared tools alongside the pack's own. The connection's initialize response states its exact scope, and is the authoritative answer for a given day.

This is deliberate, not multiplexing by accident. The meta-tools are what let a scoped connection answer a question this pack does not cover — via ask_pipeworx, which routes across the whole catalog — without you adding a second MCP server. There is currently no way to mount a pack endpoint without them; if the extra schemas cost you more context than the routing is worth, connect to the full gateway once rather than to several pack endpoints.

Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:

json
{  "mcpServers": {    "pipeworx": {      "url": "https://gateway.pipeworx.io/mcp"    }  }}

Both URLs reach the same gateway and the same 1865+ data sources. The only difference is which pack's tools are listed directly; ask_pipeworx reaches all of them from either one.

No MCP client? Call it over HTTP

bash
curl -X POST https://gateway.pipeworx.io/v1/tools/medicare_physician_payment \  -H 'Content-Type: application/json' \  -d '{"code":"99213","locality":"CA"}'

No account needed for the first calls. Inspect any tool: GET https://gateway.pipeworx.io/v1/tools/medicare_physician_payment. Find one: POST https://gateway.pipeworx.io/v1/tools/search_packs with {"query":"..."}.

Run it from npm

@pipeworx/mcp-medicare-pricing is a local stdio bridge to the Pipeworx gateway: your MCP client starts it with npx, and it forwards every call to the same https://gateway.pipeworx.io/medicare-pricing/mcp endpoint shown above. It needs network access; there is no offline mode.

json
{  "mcpServers": {    "medicare-pricing": {      "command": "npx",      "args": ["-y", "@pipeworx/mcp-medicare-pricing"]    }  }}

Without a key it calls anonymously, capped at 50 calls a day. A free account raises that to 200 a day: get a key at https://pipeworx.io/signup and add "env": { "PIPEWORX_API_KEY": "<your key>" } to the server entry. The key is sent as a Bearer token, the same way a hosted connection sends it, so calls are metered and billed exactly as they are there.

It lists only this pack's tools by default. Set PIPEWORX_META_TOOLS=1 to also list ask_pipeworx and the other gateway-wide tools.

Behind an HTTPS proxy: Node's built-in fetch does not read HTTPS_PROXY by default. Newer Node releases honour it when NODE_USE_ENV_PROXY=1 is set; check your Node version's documentation.

Using with ask_pipeworx

Instead of calling tools directly, you can ask questions in plain English — this works on the pack endpoint above as well as on the full gateway:

ask_pipeworx({ question: "your question about Medicare Pricing data" })

The gateway picks the right tool and fills the arguments automatically.

More

License

MIT

来源：README.md，提交 e7fb0fc

工具

56

ask_pipeworx

Ask Pipeworx

PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 7,174 tools across 1863 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1". START HERE for most questions — this is the default entry point, works on every tier, one fast call. Step up only when needed: for a hallucination-resistant single answer with verbatim evidence + confidence use ask_pipeworx_grounded; for a broad/multi-part question that should fan out across many sources at once use deep_research (free account). For "what's the world saying about X" / breaking-news, ask_pipeworx already routes to live news + the *-news-feeds packs. Optionally pass packs:["slug","slug"] to restrict routing to specific sources ("answer this using THESE packs"); omit it for full-catalog routing.

输入参数qasktextinputpacksquerypromptmessagequestion必填

ask_pipeworx_beta

Ask Pipeworx Beta

Beta version of ask_pipeworx: identical universal router (same 7,174 tools, same arguments, same response shape) with candidate routing improvements enabled live whenever one is under test. No candidate is active right now (the last was retired on outcome evidence 2026-07-26), so this currently matches ask_pipeworx exactly. Use it exactly like ask_pipeworx when you want the newest routing; results are compared against the stable router to decide what merges. Falls back to nothing — this IS a full working router, just the experimental edge.

输入参数qasktextinputpacksquerypromptmessagequestion必填

ask_pipeworx_grounded

Ask Pipeworx — Grounded

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 7,174 across 1863 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. An "llm_error" also carries refusal_cause ("wall" = our time budget ended before any model call was made; "route"|"threw"|"unparseable"|"wall_after_failure" = a model call failed); refusal_reason itself is unchanged. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

输入参数qasktextinputquerypromptmessagequestion必填prefer_tool

search_within

Search Within a Source

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

输入参数text必填limitquery必填

deep_research

Deep Research

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1863 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 7,174 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

输入参数qasktextdepthinputquerypromptmessagequestion必填

discover_tools

Discover Tools

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

输入参数qtasklimitquery必填searchdescription

resolve_entity

Resolve Entity

"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns `figi_candidates` to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under `unresolved` rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

输入参数type必填value必填country

compare_entities

Compare Entities

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + operating income + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

输入参数type必填values必填

subscribe

Subscribe to Alerts

Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id. Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions). Supported types: "sec_8k" (8-K filings matching ticker + item codes — e.g. items:["5.02"] = officer change), "polymarket_edge" (Polymarket↔Kalshi cross-venue mispricings — params:{topic:"fed"}), "fred_series" (new FRED observations — params:{series_id:"UNRATE"}), "patent_grant" (new USPTO grants for an applicant), "clinical_trial" (new/updated ClinicalTrials.gov studies), "form_d" (new SEC Form D fundraise notices — params:{state?, industry?, min_amount?, exemption?}, all optional), "news_keyword" (GDELT keyword news alerts, metadata+link only — params:{query, lang?, domains?}), "job_post" (new public job-board postings — params:{ats?, board_token?} or {domain?}, plus optional title_query/location_query), "tool_diff" (re-runs any read-only tool on a schedule and fires on change — params:{tool, args?, fields[], schedule}), "new_entity" (new business registrations from a state business-entity registry — params:{state:"CO", name_query?, entity_type?}), "tech_change" (a watched domain's website technology stack adds or drops a technology — params:{domains:["acme.com"], technologies?}). Delivery channels: feed (always on — pull via recent_alerts or GET registry.pipeworx.io/alerts.json), and optionally email (set delivery:{email:"[email protected]"}) or sms (delivery:{sms:"+15551234567"} — phone must be verified at /account first; 10/day cap).

输入参数type必填params必填delivery

unsubscribe

Unsubscribe from Alerts

Cancel a subscription by id. Ownership is enforced — you can only cancel your own subscriptions. The row is deactivated (not deleted) so its historical events stay available via recent_alerts.

输入参数id必填

list_subscriptions

List Subscriptions

List the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.

输入参数include_inactive

recent_alerts

Recent Alerts

Pull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed — each carries source, citation_uri (pipeworx:// when available), and the raw event payload. Filter by type (e.g. "sec_8k") and/or since (ISO timestamp). Set mark_read:true to flag returned events read so the next call only shows newer ones. Polls work fine; the same feed is also at GET registry.pipeworx.io/alerts.json for scripts and dashboards.

输入参数typelimitsincemark_readunread_only

entity_profile

Entity Profile

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when `value` was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (the latest ANNUAL Revenues, NetIncomeLoss, Cash, diluted EPS, capex and operating cash flow, each with its period_end, SEC form and the accession of the filing it came from — plus EntityCommonStockSharesOutstanding, which is the newest COVER-PAGE share count the filer has published rather than an annual one, so its period_end is a cover date after the quarter closed and it is the right per-share denominator; a filer tagging a fallback concept appears under the name of that tag instead, e.g. PaymentsToAcquireProductiveAssets instead of PaymentsToAcquirePropertyPlantAndEquipment); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. `sources_used` / `sources_failed` say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. `sources_skipped` is the third state: a leg we deliberately did NOT run, each en

输入参数type必填value必填country

recent_changes

Recent Changes

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

输入参数type必填since必填value必填

validate_claim

Validate Claim

"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

输入参数claim必填tolerance_pct

company_facts

Company Facts

TYPED, DETERMINISTIC financial facts for a US public company for an EXPLICITLY NAMED reporting period — "Apple revenue for fiscal 2023", "Walmart net income FY2026 Q3", "Microsoft cash at the end of fiscal 2024". PREFER OVER entity_profile / get_company_financials whenever the period matters: those answer "the most recent figures" and will happily hand back FY2025 when you asked about FY2019, and neither separates a discrete quarter from a year-to-date figure. This one refuses instead — it NEVER substitutes the latest period for the period requested, NEVER returns 0 for missing data, NEVER lets a 9-month YTD number answer a quarterly question, and NEVER converts a currency. Every answer carries the exact us-gaap concept it came from, what that concept MEASURES (NetIncomeLoss excludes non-controlling interests, ProfitLoss includes them — not synonyms), the accession number and a link to the filing on sec.gov, the restatement trail of any superseded figures, and a contract + derivation version to pin against. Fiscal periods are the FILER'S OWN, anchored on their fiscal-year end, so Walmart's year ending 2026-01-31 is FY2026 and Apple's ending 2025-09-27 is FY2025. Attributes: revenue, net_income, cash, gross_profit, operating_income, eps_diluted, capex, operating_cash_flow, long_term_debt, shares_outstanding (the dei COVER-PAGE share count, so its period end is the cover date, after the quarter closed — that is the most current count the filing carries). Units are not all USD: shares_outstanding is in `shares` with currency null, eps_diluted in `USD/shares`. A discrete quarter the filer never filed — every Q4, and Q2/Q3 of cash-flow lines, which are filed year-to-date — is DERIVED as YTD(q) minus YTD(q-1) and comes back with `reported: false`, the subtraction named in `derivation` (fy_minus_9m / ytd_9m_minus_6m / ytd_6m_minus_3m) and both legs with their filings in `derived_from`; test `reported` if you will only accept a figure lifted from a filing. Per-share and bal

输入参数as_ofbasisperiod必填company必填max_ageattribute必填freshnessrestatementexclude_publishers

scan_dependency

Scan Dependency

Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

输入参数package必填version

bet_research

Bet Research

Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-kristi-noem-win-the-2028-republican-presidential-nomination"), a polymarket.com URL, or a question text. A dated slug ("...-by-june-30-2026") still resolves after it settles — the resolver retries a closed-market lookup rather than giving up on the bare one — so either dated or undated works; prefer whichever slug you actually have. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets the

输入参数depthmarket必填include_raw

polymarket_arbitrage

Polymarket Arbitrage

Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FEES: every opportunities[] row and partition_check.arbitrage carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and fee_categories[]. BOTH cost components are modeled: Polymarket's own per-category TAKER FEE (fee = shares × rate × p × (1-p), rates crypto 0.07 / sports-economics-culture-weather-other 0.05 / finance-politics-mentions-tech 0.04, geopolitics and world events fee-free; verified against Polymarket's own d

输入参数eventtopic

polymarket_edges

Polymarket Edges

Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price. Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets. FIVE MODEL FAMILIES grouped into three response segments under by_segment: (1) MODEL_DRIVEN — crypto_price (lognormal barrier from 90d FRED log-returns) and news_momentum (GDELT 7d/21d article-volume ratio, soft signal w/ halved Kelly). (2) STRUCTURAL_ARBITRAGE — partition_overround on mutually-exclusive events; per-leg favorite-longshot bias correction with per-sport α (tennis 1.02, soccer 1.10, MMA 1.15, default 1.0); placeholder-slug filter drops will-person-X / will-team-Y / will-manager-Z / will-someone-else- backstops; partitions with >20% placeholder fraction skipped entirely. (3) CONCENTRATED_LONGSHOT — basket trade when one leg ≥75% AND ≥2 longshots ≤8% AND portfolio return ≥25:1; rare-by-design (gates relaxed Run 8 from prior 85%/5%/50:1). EVERY OPPORTUNITY carries edge_pp_net (net of slippage AND Polymarket's own taker fee — fees_pp_applied itemises the fee component; see fees.ts for the published per-category schedule), kelly_fraction + kelly_fraction_half (capped at 0.25), market.liquidity, market.spread_pp, market.volume, plus a 24h-move warning ("Market moved X.Xpp in 24h") when the recent move alone exceeds the edge — your edge may already be in the price. TRADEABLE-EDGE KNOBS: min_liquidity / max_spread_pp drop opportunities where edge isn't realizable; min_partition_leg_kelly filters partitions by best per-leg Kelly. RESPONSE TOP-LEVEL: by_segment{model_driven,structural_arbitrage,concentrated_longshot}, fed_candidates/fed_note (Fed bets surface here, excluded from ranking — 1m-T vs EFFR signal is unreliable at meeting-month horizons without paid OIS/SOFR-futures data), and _diagnostics{concentrated_longshot:{...funnel counters},category_counts,filter_skips} so callers can see WHY a segment is empty (top-N stale, all candidates failed gates, k

输入参数limitwindowmin_kellymin_edge_ppslippage_ppmax_spread_ppmin_liquiditycategory_filtermin_partition_leg_kelly

polymarket_kalshi_spread

Polymarket–Kalshi Spread

Cross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says so. TWO MODES: (1) `topic` — 11 pre-mapped macro subjects ("fed", "btc", "eth", "cpi", "gdp", "sp500", "recession", "next_pope", "next_uk_pm", "next_israel_pm", "2028_president") auto-fetch the matching event on each venue. You do NOT have to use those exact keys: the topic is resolved through aliases and keywords, so "bitcoin", "fed rate decision", "inflation", "s&p 500" and "next pope" all land on the right subject, and `resolution.topic_matched_by` tells you whether it was an exact key, a known alias, a phrase found inside a longer question, or a single-keyword guess — treat "phrase" and "token" as a GUESS at what you meant. An unresolvable topic returns error:"mapping_failed" with mapping_stage:"topic_unrecognized" and known_topics[]; it never silently falls back to a default subject. (2) explicit `kalshi_event_ticker` + `polymarket_event_slug` for custom pairings — BOTH modes run the identical token-overlap matcher, so the same disclosures apply to both. `resolution` is returned in BOTH modes and says how each side's identifier was picked (which Kalshi series was queried, how many events came back, whether the chosen one had quoted markets; which Polymarket search query ran and why that event won). Fleet #2064: when two Polymarket candidates tie on resolution time `polymarket_selected_by` now SAYS so, names every tied slug, names the tie-break that actually decided it (the candidate whose metric_type matches the Kalshi series, else lexicographic slug order), and states whether the winner's metric matches the Kalshi series — it used to assert "picked the soonest-resolving" byte-identically on calls that returned DIFFERENT events, because the tie was settled by upstream fetch arrival order

输入参数topickalshi_event_tickerpolymarket_event_slug

polymarket_fill_risk

Polymarket Fill Risk

Realizable-vs-theoretical edge check against live CLOB order-book depth — AND Polymarket's own taker fee, which is never zero and is folded in rather than left for you to add (fleet #3446: r/PredictionsMarkets flagged a "clean" fill that quietly omitted it as optimistic in the direction that loses money). REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict (clean|degraded|cannot_fill), fee_schedule ({category, taker_rate, exponent, maker_rebate_rate, fees_enabled, basis} — read off THIS market, never assumed free), fee_usd/fee_pp at the actual VWAP fill, breakeven_win_rate_taker (the win probability your side needs just to cover price + fee — ~51.8% on a 50c crypto taker fill, not 50%), breakeven_win_rate_maker (no fee charged to makers — any rebate is in fee_schedule.maker_rebate_rate), and all_in_cost_pp = slippage_pp + fee_pp. BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio and profit_usd — both NET of every leg's own Polymarket taker fee (capture_ratio_gross/profit_usd_gross keep the pre-fee figures; per_leg[].fee_usd itemizes each leg, summed as fees_usd_total), per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and

输入参数sideeventmarketsize_usd

polymarket_edge_tracker

Polymarket Edge Tracker

Edge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers "how long has this edge existed and is it shrinking?" — a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take). Args: days (lookback, default 14, max 30), window (snapshot family, default "1wk"). RESPONSE: tracked[] = every opportunity in the LATEST snapshot with its full edge_pp_net time-series across prior snapshots, first_seen, trend (new | widening | stable | decaying) and decay_pp_per_day (both computed on |edge_pp_net| — the value itself is signed by trade direction, negative = SELL YES); expired[] = opportunities that appeared in earlier snapshots but are GONE from the latest (closed, resolved, or arbed away) with their lifespan_days — the median lifespan is your competition clock; snapshot_dates[] = which days actually have data (snapshots are written when polymarket_edges runs on a cache-miss, so gaps mean nobody scanned that day). LIMITS: history depth is bounded by the 60-day snapshot TTL and starts from when snapshotting was enabled; decay numbers come from daily closes of edge_pp_net (net of default slippage AND Polymarket's own taker fee — see polymarket_edges), not intraday.

输入参数dayswindow

kalshi_weather_edge

Kalshi Weather Edge

Prices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob (mid), forecast_prob, and edge_pp per strike, plus the settlement clause verbatim. BACKTEST (`backtest_days: N`): scores an archived gridded forecast against the market on settled days and returns brier_market vs brier_forecast with a plain-English `verdict`, so the edge is MEASURED rather than asserted. READ THE WARNINGS — they are not boilerplate. (1) These markets DO NOT settle on the NWS. They settle on The Weather Company (weather.com) at a Kalshi station code such as CLINYC, which the response quotes verbatim; so part of every edge_pp is NWS-vs-Weather-Company disagreement about the same day at the same station, which is not mispricing and not tradeable. `settlement_vs_forecast_basis_f` from backtest mode is that part as a number. (2) The station is DERIVED from the settlement clause, never from the city name: Chicago settles at MIDWAY and New York at CENTRAL PARK, so a city-centre forecast would misprice a whole ladder. A station that cannot be resolved yields rows with no forecast and a reason, never a guessed coordinate. (3) forecast_prob assumes a normal distribution around the NWS high whose width is ASSUMED, not fitted (stated in `distribution_assumption`) — run backtest mode to see whether it is calibrated. (4) edge_pp is gross: no Kalshi fees, no bid-ask. MEASURED RESULT, AND IT IS NOT THE FLATTERING ONE: on the first backtest (KXHIGHNY, 13 settled days to 2026-09-11, 58 market observations) the MARKET beat the forecast — Brier 0.1008 for the market against 0.1594 for the archived gridded forecast, lower being better. So on that sample there is NO forecast edge to sell, and a large edge_pp is more likely to be the model disagreeing with a better-informed market than an opportu

输入参数citydatemarket_typebacktest_daysseries_ticker

resolution_audit

Resolution Audit

Extract the settlement clause of a single Polymarket (.com or .us) or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at (falling back to the market TITLE when the clause itself says "the time range specified in the title" — e.g. Polymarket's 15-minute Chainlink TWAP family), the precision of the computation (e.g. "1-minute candle close" vs "60-second trailing average" vs "twap_60s_lookback"/"twap_30s_lookback" vs "election outcome"), comparison_basis — what is compared to what (end_average_vs_start_average | close_vs_open; orthogonal to precision, since venues can share this comparison shape while computing each endpoint differently), the evidence standard (official_source | consensus_reporting | any_credible_report | unspecified), and void_handling (cancellation/postponement settlement — reused verbatim from bet_research's cancellation_rule detector, not re-derived). Parses Polymarket's `description` field (fetched via polymarket_market, or via polymarket_us_search for the .us venue) or Kalshi's `rules_primary` + `rules_secondary` fields (fetched via kalshi_market) with regex + a small vocabulary — no LLM pass, so an unusual clause reports confidence:"low" rather than a guess. Pass `market` as a Polymarket slug/URL (either venue) or a Kalshi market ticker (e.g. "KXBTCD-26SEP1317-T66999.99"); a Kalshi EVENT ticker (e.g. "KXBTCD-26SEP1317") also works — it picks one representative market under that event, since the settlement mechanism is normally shared across all strikes/legs in one event. Use this before treating a polymarket_kalshi_spread row as a real arbitrage: two ladders that look alike can settle on different sources (Polymarket.com's Chainlink TWAP stream vs Kalshi/Polymarket US's CF Benchmarks BRTI), at different times, or with a different lookback window — this tool is how you check. Pair with resolution_diff to compare two markets directly. KNOWN GAP: idiosyncratic phrasing that doesn't match the vocabula

输入参数venue必填market必填

resolution_diff

Resolution Diff

Field-by-field diff of TWO markets' settlement clauses (one from each of `a` and `b`; either can be Polymarket .com, Polymarket US, or Kalshi) — runs resolution_audit on both sides and compares source, settle time, precision, comparison_basis, and evidence standard. Returns `equivalent`: "true" only when both sides parsed with enough confidence to compare AND no field conflicts; "false" when a specific conflict was found (differing_fields names which — e.g. ["source","settle_time"] for a Polymarket Bitcoin market settling on Binance's 1-minute candle at noon ET versus a Kalshi KXBTCD market settling on CF Benchmarks' BRTI 60-second average at 5pm EDT — SAME asset, DIFFERENT contract; or ["source","precision"] for Polymarket.com's Chainlink 15-minute TWAP clause (precision "twap_60s_lookback") against Kalshi's 15-minute KXBTC15M clause (precision "60-second trailing average" off CF Benchmarks' BRTI) — different source and a different lookback mechanism, even though BOTH compare an end-of-window average to a start-of-window average, so comparison_basis is EQUAL there and correctly NOT in differing_fields — verified live 2026-10-22, fleet #3447); "unclear" when one or both sides could not be confidently parsed (an absence of evidence is not evidence of equivalence — read raw_clause yourself in that case). comparison_basis is flagged as differing whenever both sides resolved one and they disagree, independent of whether source/precision also matched. Only flags source/evidence_standard as differing when BOTH sides gave a SPECIFIC comparable answer — a named source (e.g. "Associated Press, Fox News, NBC") against a generic one (e.g. Kalshi's "consensus of media organizations") is treated as the same evidence standard, not a conflict, since that is standard election-market boilerplate on both venues. Use this before sizing a polymarket_kalshi_spread pair as a real cross-venue arb, or standalone to sanity-check any two markets you suspect settle on different things.

输入参数a必填b必填

release_calendar_markets

Release Calendar Markets

JOIN of the official release calendar (econ data, the FOMC, FDA decisions, SEC rules) against LIVE Polymarket/Kalshi markets — which scheduled releases land in the next N hours, and which live markets resolve on them. This is a POSITIONING tool, not a speed product: results are cached like every other pack (≤ 60s TTL) and there is no push/webhook — do not use this to try to beat a release, use it to see what is coming and what is already priced. CATEGORIES: econ (CPI, Employment Situation/jobs report, GDP, PCE, PPI, retail sales, housing starts, jobless claims — via fred_release_dates per known release_id, since FRED's own cross-release calendar mostly returns recent actuals, not future dates), fed (the next FOMC meeting's rate decision, via fomc_calendar), fda (PDUFA action dates + FDA advisory-committee meetings, via pdufa_catalysts / fda_adcom_calendar), sec (SEC final rules whose own DATES clause names an effective date in the window, via federal-register recent_rules — usually finds nothing in a short window since SEC rules typically take effect 30–60 days out, which is an accurate answer, not a bug), court (ALWAYS EMPTY today — court-listener has no forward-looking scheduled-hearing calendar, only filing/termination dates, so this category returns zero releases with unsupported:true rather than fabricate one). Omit `categories` or pass "all" for every category. MATCHING AND ITS HONESTY CONTRACT: every release is returned even when it has ZERO matched markets — a release is never dropped just because nothing on Polymarket or Kalshi resolves on it (most FDA/SEC releases will show markets:[]; that is signal, not a gap). Every matched market carries resolves_on_this_release: "true" (the venue's own close/end date sits within ~36h of the release AND the question passed a subject filter — econ and fed only), "likely" (same subject filter, but the venue closes days away from the release date), or "unclear" (a keyword hit with no date to anchor against — always true f

输入参数hourscategories

prediction_calibration

Prediction Calibration

REALIZED win rate by shown price and minutes elapsed for short-horizon crypto up/down markets, net of taker fee — read from our own settled-outcomes archive, not from live prices. Answers the one question no venue will: "a side shown at 82 with five minutes left, how often did it ACTUALLY win?" Returns a grid of (price bucket x minute elapsed) -> n, realized_win_rate_pp, mean_shown_price_pp, calibration_gap_pp (realized minus shown — zero means the book is calibrated), breakeven_win_rate_pp (the shown price plus the taker fee that was actually in force for that market), and net_edge_pp (realized minus breakeven, which is what a taker would have kept). WHY AN ARCHIVE EXISTS AT ALL: a settled 15-minute window is exactly what these venues stop serving. Measured 2026-10-22, a polymarket.com window drops out of the default market listing within minutes of closing (its trade tape and resolution stay retrievable by a closed-market lookup, which the collector now uses), and the other venues serve settled windows only briefly or thinly. So this tool reads a local archive collected window-by-window as they settle. THE THREE VENUES ARE NEVER POOLED. all three compare an END 60-second average with a START 60-second average, but on different feeds: polymarket.com on a Chainlink TWAP, polymarket-us and Kalshi on a CF Benchmarks BRTI simple average. The feeds disagree near the threshold (measured 2026-10-24 on btc: polymarket.com and Kalshi settled differently on 1.6% of windows both hold, polymarket-us and Kalshi on none), so venue:"all" returns one grid PER VENUE and `venues_pooled` is always false. EVERY RATE CARRIES ITS 95% INTERVAL, AND EVERY EDGE SAYS WHETHER IT SURVIVES IT. Each cell gives realized_win_rate_ci95_lo/hi_pp (Wilson, so it never reports an impossible win rate above 100), the same interval carried onto net_edge_ci95_lo/hi_pp, and net_edge_distinguishable_from_zero. READ THAT BOOLEAN BEFORE net_edge_pp: at these sample sizes most cells are false, meaning the inte

输入参数assetvenueminutehorizonbucket_ppside_convention

updown_venue_compare

Updown Venue Compare

The same 15-minute crypto "up or down" window priced on polymarket.com, Polymarket US and Kalshi, side by side, with whether each pair is actually the same contract. Pass asset (btc | eth | sol | xrp | bnb) and window ("current" default, "next", "previous", or the window START as ISO such as 2026-10-27T14:15:00Z, on a 15-minute boundary). Per venue: question, Up bid / ask / mid / last, spread_pp, quote_state, volume WITH ITS UNIT, open interest, liquidity, settlement source, comparison basis, when it settles. THE VENUES ARE NOT ONE CONTRACT. Kalshi and Polymarket US settle on CF Benchmarks' BRTI (60-second average at the end against the same at the start): the same bet, so a gap between them is a real disagreement. polymarket.com settles on a Chainlink 60-second TWAP, end against start: same comparison shape, different feed, so a gap against either is partly the contract. `pairs[]` gives equivalent ("true"|"false"|"unclear"|"not_comparable") from resolution_diff for every pair, material_differences, mid_diff_pp (a minus b, in percentage points) and read_as in words. Measured over 30 days of settled windows the Chainlink and BRTI feeds settle a window differently about 1.2-1.8% of the time, almost entirely windows that ended near the threshold (see updown_divergence_rate). `net_spread` is computed only for the pair that is equivalent: the gap in mid terms and at top of book (buy Up where cheaper at the ask, Down where Up is dearer at 1 minus the bid) after BOTH venues' taker fees, each fee carrying its verified-as-of date. Top-of-book size is shown where the venue publishes it (Polymarket US); the Kalshi figure is not in its market payload, so read its order book before sizing. A venue with no market for the window is {status:"not_listed", reason}; one that could not be read is {status:"unavailable", refusal} -- never an empty object, and never the same thing. A one-sided book (typical Polymarket US just before a window is decided) has quote_state "bid_only", a null

输入参数assetwindowhorizon

updown_divergence_rate

Updown Divergence Rate

How often two venues settled the SAME 15-minute crypto window differently, over every settled window on record. Compares polymarket.com (Chainlink TWAP) with Kalshi and with Polymarket US (both CF Benchmarks BRTI), and Kalshi with Polymarket US as the same-feed control. Per pair: windows both venues settled, divergent count, the rate with its 95% Wilson interval (withheld under 30 paired windows), which direction they split, and what was excluded (unsettled on a side, 50-50). A HEADLINE RATE IS A CONCENTRATION QUESTION, so every pair also carries `concentration` (UTC days and hours the divergent windows fell in, the busiest day, the top-3-day share, and a plain reading) and `by_how_decided` (the rate by how far the reference venue's mid was from 50 at its last priced minute: the rate is a fraction of a percent for windows that were decided by the end and tens of percent for the ones that were not). `divergent_path` gives the mean Up mid by minute on both venues over the divergent windows only, split by who said Up, so the "early spike that fades" story can be checked rather than assumed. Measured on the archive 2026-10-27 over 30 days of btc: polymarket.com vs Kalshi diverged on 48 of 2,847 windows (1.69%, interval 1.27-2.23), spread over 27 of 31 UTC days, 0.19% of windows decided by the end and 17-29% of the ones still undecided; Polymarket US vs Kalshi on 0 of 1,838. asset btc | eth | sol | xrp | bnb (bnb has no pair: Kalshi only); days 1-90 (default 30), capped by what the archive holds (it starts 2026-09-22 for polymarket.com and Kalshi, 2026-10-07 for Polymarket US). Read-only; cheap enough to call, it reads several thousand archive rows.

输入参数daysasset

archive_evidence

Archive Evidence

What did a web page say on a given date? Finds the archived capture of a URL nearest that date across the Internet Archive Wayback Machine, Common Crawl and Arquivo.pt, reads it, and returns the readable text (HTML stripped) with the capture timestamp, a link to the archived copy, and the GAP between the date asked for and the capture actually used (e.g. "Captured 1.5 day(s) before the requested date"). One call replaces the find-capture, fetch-record, parse-HTML chain. If no archive holds a capture within max_gap_days (default 30) it answers status "no_capture" and names the nearest capture before and after the date with their gaps, so a distant capture is never passed off as the page on that date. Every archive's outcome is listed in sources[]; one that timed out or errored says so with its upstream status, and if all of them failed the answer is error "archives_unavailable", not no_capture. Captures are immutable and cached by capture id; served_from says whether the text came from cache or upstream. Use archive_compare to see what changed between two dates.

输入参数url必填date必填max_charsmax_gap_days

archive_compare

Archive Compare

What changed on a web page between two dates? Resolves the archived capture nearest each date (Internet Archive Wayback Machine, Common Crawl, Arquivo.pt), extracts the readable text of both, and returns a text-level diff: the segments removed and added, a similarity score, and both capture timestamps with their gaps from the requested dates. It diffs readable text, not markup, so a restyled page with the same words reads changed:false. Identical captures answer changed:false with the reason (same capture, identical content digest, or identical text), never an empty diff. A date with no capture within max_gap_days (default 30) answers status "no_capture", naming which side is missing and the nearest captures to it. Typical uses: how a privacy policy, terms of service, pricing or product page changed; whether a statement was on a page at a given time.

输入参数url必填date_a必填date_b必填max_gap_days

research_row

Research Row

Claygent-style per-row research: answer ONE question about ONE company and return a JSON object matching your output_schema, with every field wrapped in its own evidence — {value, evidence_url, quote, status}. status is NEVER self-reported by the model: "found" means this code independently verified the model's quote is a VERBATIM substring of text actually fetched; "inferred" means a value was given but the quote could not be verified; "not_established" means no source answered the field, and its value is always null — never guessed, never a blended score. Sources: "web" fetches the company's own site (entity.domain required — homepage, /about, /leadership) via fetch_url; "catalog" checks entity_profile (SEC EDGAR; resolved:false for a private company is expected, not a failure); "both" (default) tries both. max_cost (USD, default 0.02, max 0.50) scales how many pages are fetched and refuses up front if it is too small to pay for even one extraction call (floor $0.002) — never silently overspends. A row where every source comes back empty costs nothing: every field is mechanically not_established with NO model call made. For a boolean/derived field (e.g. "does this company sell to healthcare"), the quote only has to SUPPORT the value — a descriptive sentence about customers/products counts, the source need not state the answer verbatim. leadership_page_status ("fetched"|"not_found"|"not_attempted", plus leadership_note) says plainly whether a leadership/team page was actually read, so a named-person field (head_of_sales) coming back not_established is distinguishable from "this company just doesn't publish one" vs. "we never checked". model_output_empty:true means the model returned nothing usable (a transport/budget problem) — every field is not_established for THAT reason, never read as 20 individual "no" judgments. Use with a fixed question + schema across many rows for "Claygent over N companies with a hard cost cap" (composes with batch callers). For picking f

输入参数entity必填sourcesmax_costquestion必填output_schema必填

classify_row

Classify Row

Cheap categorization: given a company and a list of candidate labels, decide which labels apply — the most common GTM/AI usage shape (tagging a list by industry, ICP fit, etc.), one small model call. Same evidence contract as research_row: each label comes back {label, applies, evidence_url, quote, status: found|inferred|not_established} — "found" only when the model's quote is independently verified as a verbatim substring of the text used. Pass text directly (cheapest — no fetch) or entity.domain to fetch the homepage once as a fallback. No text and no domain means every label is not_established with no model call made.

输入参数textentity必填labels必填

suggest_questions

What Can I Ask Pipeworx?

What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass `topic` (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

输入参数topic

generate_llms_txt

Generate llms.txt

Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

输入参数url必填max_links

ai_visibility_check

AI Visibility Check

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

输入参数entity必填models_apiKeycontext

scan_competitor_ai_presence

Scan Competitor AI Presence

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

输入参数models_apiKeycontextentities必填

pipeworx_feedback

Send Pipeworx Feedback

Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

输入参数typecontextmessageclaim_token

remember

Remember

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

输入参数kvkey必填datanametextlabelvalue必填content

recall

Recall

Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

输入参数kkeynamelabel

forget

Forget

Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.

输入参数kkey必填namelabel

medicare_physician_payment

Medicare Physician Payment

What does Medicare pay a doctor for a procedure code, in a specific place? Returns the relative value units (work, practice expense, malpractice) and the actual dollar payment for a HCPCS/CPT code, adjusted for the Medicare LOCALITY — payment varies geographically, not just by state. Answers "how much does Medicare pay for 99213", "what is code 27130 worth in Los Angeles", "reimbursement rate for an office visit in Texas", and fee-schedule or contract-rate benchmarking. Gives both the facility and non-facility amount (a procedure pays less in a hospital, because the hospital bills the overhead separately), the global period, and the status code saying whether the code is separately payable at all. States the fee-schedule YEAR and the locality it resolved to on every response, because a rate quoted for the wrong year or locality is a wrong number. Sourced from the CMS Physician Fee Schedule relative value files.

输入参数code必填yearlocalitymodifier

medicare_lab_rate

Medicare Lab Rate

What does Medicare pay for a laboratory test? Returns the national Clinical Laboratory Fee Schedule rate for a lab HCPCS/CPT code — lab tests are paid from their own national fee schedule rather than the physician schedule, so there is one rate and no locality adjustment. Answers "how much does Medicare pay for a comprehensive metabolic panel", "CLFS rate for 80053", "what does a lipid panel reimburse". States the year and quarter of the schedule used, since lab rates are republished quarterly. Sourced from the CMS Clinical Laboratory Fee Schedule.

输入参数code必填year

medicare_lab_test_lookup

Medicare Lab Test Lookup

Resolve a branded molecular or genomic lab test by its commercial name (e.g. Signatera, Guardant360 CDx, FoundationOne CDx, FoundationOne Liquid CDx, Tempus xT CDx, DecisionDx-Melanoma, Envisia, Shield) to its CPT PLA / HCPCS billing code, the lab that runs it, what Medicare pays for it now on the Clinical Laboratory Fee Schedule, and the MolDX local coverage determinations (LCDs) and billing-and-coding articles for its test category. Answers "what does Medicare pay for Signatera", "what is the PLA code for Guardant360", "which MolDX LCD covers this test". Also accepts a lab name (e.g. "Natera") or a code. Also covers non-ADLT branded MAAA tests (Oncotype DX, Prosigna, MammaPrint, EndoPredict, Breast Cancer Index, Cologuard, Prolaris, Decipher, Afirma, ConfirmMDx, 4Kscore) and any test whose lab registered its code in the NCBI Genetic Testing Registry. Resolves in order: the CMS list of Advanced Diagnostic Laboratory Tests (ADLTs), a brand-to-code table of MAAA tests with MolDX billing articles, then NCBI GTR live; a test found in none returns brand_not_found naming every source checked. Sourced from CMS (ADLT list, Clinical Laboratory Fee Schedule, Medicare Coverage Database) and NCBI GTR.

输入参数test必填include_retired

medicare_dmepos_rate

Medicare Dmepos Rate

What does Medicare pay for a piece of durable medical equipment, prosthetic, orthotic, or medical supply (DMEPOS)? Returns the fee-schedule amount for a HCPCS Level II code (E/K/L/A-codes — CPAP machines, wheelchairs, ostomy supplies, braces, oxygen equipment...) in a given US state, with SEPARATE rural and non-rural amounts, since Medicare pays DMEPOS by state rather than by the physician-fee-schedule locality. Answers "how much does Medicare pay for a CPAP machine (E0601)", "DMEPOS rate for a wheelchair in Texas", "what is the rural rate for this HCPCS code in Montana". Unlike medicare_physician_payment, this DOES include the HCPCS code description — DMEPOS uses HCPCS Level II, which CMS itself maintains and is not AMA copyright (CPT/HCPCS Level I is). States the fee-schedule YEAR and RELEASE (quarterly-ish: A=January, B=April, C=July, D=October) used on every response, and the modifier resolved (many DMEPOS codes price differently for rental "RR" vs. new purchase "NU" vs. used "UE" — pass one if you know it, otherwise the first modifier on file for the code is used and stated).

输入参数code必填yearstatereleasemodifier

medicare_opps_rate

Medicare Opps Rate

What does Medicare pay a HOSPITAL OUTPATIENT department for a procedure, device, or drug code? Returns the status indicator, Ambulatory Payment Classification (APC), relative weight, national payment rate, and beneficiary copayment for a HCPCS/CPT code under the Hospital Outpatient Prospective Payment System (OPPS) — the schedule that applies when the same service is billed by a hospital's outpatient department rather than a physician office (medicare_physician_payment) or a lab (medicare_lab_rate). Answers "what does Medicare pay for CPT 33289 in a hospital outpatient setting", "what is the APC for C2624", "is this device on pass-through status", "what is the OPPS payment rate and copay for this code". Flags device_pass_through and drug_or_biological_pass_through — whether the code is CURRENTLY in a CMS pass-through category (status indicator H or G), meaning it is paid separately, outside the normal APC amount, rather than packaged into the procedure it goes with. A packaged code (status indicator N, among others) still has a valid status_indicator but no apc/payment_rate_usd, because OPPS pays for it only as part of whatever procedure it is billed with — that is a normal result, not a missing one. States the fee-schedule YEAR and QUARTER (Q1=Jan, Q2=Apr, Q3=Jul, Q4=Oct) used on every response. Sourced from CMS, OPPS Addendum B.

输入参数code必填yearquarter

medicare_code_units_limit

Medicare Code Units Limit

How many units of a procedure code will Medicare pay for one patient in one day? Returns the NCCI Medically Unlikely Edit (MUE) — the maximum units allowed — with the adjudication indicator saying whether the limit is absolute or can be overridden with documentation, and the rationale for it. Answers "what is the MUE for 99213", "how many units of this code can I bill", "why was my claim denied for units". Available for practitioner, outpatient hospital and DME settings, which have different limits for the same code. States the quarterly edit vintage in force. Sourced from the CMS National Correct Coding Initiative.

输入参数code必填setting

check_code_pair

Check Code Pair

Can two procedure codes be billed together on the same day? Checks the CMS National Correct Coding Initiative procedure-to-procedure (PTP) edit for a pair of HCPCS/CPT codes and returns the modifier indicator saying whether the pair can never be unbundled, can be unbundled with an appropriate modifier and documentation, or carries no edit in the loaded data. Answers "can I bill 99214 and 93000 together", "is there an NCCI edit between these two codes", "do I need a modifier for this code combination". Built from CMS's FULL NCCI PTP edit baseline (2026q3, v322r0), not just a rolling change window — a pair not found here has never had a CMS-recorded PTP edit as of the loaded vintage. The response always states the loaded vintage and setting resolved.

输入参数code_a必填code_b必填setting

explain_ncci_edit

Explain Ncci Edit

What NCCI procedure-to-procedure edits exist for a single code? Lists the other codes this HCPCS/CPT code has a recorded PTP edit with — whether it is the code you bill (Column 1) or the one bundled into another (Column 2) — with each pair's modifier indicator. Answers "what codes conflict with 99214", "what is bundled into this procedure", "why would this code be denied when billed with something else". Same full-baseline coverage as check_code_pair (CMS's FULL NCCI PTP edit baseline, 2026q3 v322r0, not just a rolling change window) — an empty result means no PTP edit has ever been recorded for this code, not merely "no recent change".

输入参数code必填statussetting

medicare_fqhc_pps_rate

Medicare Fqhc Pps Rate

What does Medicare pay a Federally Qualified Health Center (FQHC) per visit? Returns the CMS FQHC Prospective Payment System (PPS) national BASE payment rate for a calendar year — the figure CMS publishes once a year, before any per-FQHC Geographic Adjustment Factor (GAF) is applied. Answers "what is the FQHC PPS base rate", "how much does Medicare pay an FQHC per encounter", "FQHC PPS rate for 2025 vs 2026". Medicare pays an FQHC the LESSER of its actual charges or this PPS rate for all FQHC services furnished to a beneficiary on a day with a qualifying face-to-face visit — it is a per-DAY encounter rate, not a fee per procedure code (unlike medicare_physician_payment/medicare_lab_rate). This tool returns only the national base rate before the geographic adjustment; for the GAF-adjusted amount in a named locality use medicare_fqhc_gaf. Omit year for the newest loaded year plus the full known history (small — CMS publishes one number a year); pass year for one row.

输入参数year

medicare_gpci_lookup

Medicare Gpci Lookup

What are the Medicare geographic adjustment indices (GPCIs) for a place — with NO procedure code needed? Returns the work, practice-expense and malpractice GPCI for a Medicare payment locality (or every locality in a state), for a calendar year. Answers "GPCI for Rest of Florida locality 99", "Medicare geographic practice cost index for San Francisco", "work GPCI Manhattan 2025", "which Medicare localities does California have". Medicare pays by LOCALITY, not state (California has nine), so a state or ambiguous name returns every matching locality rather than one chosen quietly, and the resolution is stated. The work GPCI is the floored value CMS pays on (1.0 floor where it applies). Pass state ("FL" or "Florida"), a locality name ("Bakersfield"), and/or locality_number ("99"); year defaults to the newest loaded and is stated. For the FQHC-specific adjustment see medicare_fqhc_gaf; for a dollar payment see medicare_physician_payment. Sourced from the CMS Physician Fee Schedule geographic practice cost indices.

输入参数yearstatelocalitylocality_number

medicare_fqhc_gaf

Medicare Fqhc Gaf

What is the FQHC Geographic Adjustment Factor (GAF) for a place, and what does Medicare pay a Federally Qualified Health Center per visit THERE? Returns the official CMS FQHC PPS GAF for a Medicare locality and calendar year, and the GAF-adjusted per-visit rate (national base rate x GAF). Answers "FQHC PPS GAF for Orlando, Florida 2025", "CY2026 FQHC geographic adjustment Rest of California", "what is the FQHC PPS rate in Alaska". The GAF is CMS's own published number per locality (adapted from the work and practice-expense GPCIs, with the 1.0 floor), not something this tool estimates. A state returns all its localities; a city is not a locality, so name the locality or pick from the list (Florida has Fort Lauderdale, Miami and Rest of Florida). Complements medicare_fqhc_pps_rate (national base rate) and medicare_gpci_lookup (the underlying GPCIs). Years 2018-2026 loaded; the GAF-adjusted rate is returned for 2022-2026 (years whose national base rate this pack carries) and is null with a stated reason for 2018-2021. Sourced from the CMS FQHC PPS geographic adjustment factor tables.

输入参数yearlocalitylocality_number

medicare_pricing_coverage

Medicare Pricing Coverage

What Medicare pricing data is loaded and how current it is: the fee-schedule years and quarters held, how many codes are in each dataset, how many localities, and the PTP pair-edit change window. Use to check a vintage is current enough to quote before relying on a rate, and to see the scope limit on check_code_pair / explain_ncci_edit — they see only pairs that changed within the loaded quarterly window, not the full AMA-licence-gated PTP baseline.

无需输入参数。

版本历史

6
  1. v0.2.1最新Nov 3, 2026
  2. v0.2.0Oct 29, 2026
  3. v0.1.4Oct 18, 2026
  4. v0.1.3Sep 27, 2026
  5. v0.1.1Sep 25, 2026
  6. v0.1.0Sep 16, 2026

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