FreshContext

io.github.PrinceGabriel-lgtm由 GitHub 帳號 PrinceGabriel-lgtm 發布v0.5.3更新於 Oct 24, 2026

Context integrity for AI agents: evaluate freshness, confidence, provenance, and decision readiness.

已驗證Streamable HTTP可網頁執行網頁搜尋與抓取知識與記憶資料與分析

概覽

AI 產生的概覽

FreshContext 評估候選脈絡的時效性、信心度與來源,並提供唯讀轉接器從公開來源擷取帶時間戳的資料。

可以做什麼
評估呼叫端提供的候選脈絡,回傳決策、時效性、信心度、排序分數、實用性與警告,並將判定簽章記錄到帳本(evaluate_context)。從公開來源擷取帶時間戳的資料:GitHub 儲存庫與儲存庫搜尋、Hacker News、arXiv、Google Scholar、Reddit、Product Hunt、npm 與 PyPI 套件中繼資料、Stooq 股票行情、職缺、更新紀錄、GDELT 新聞、美國聯邦合約、SEC 8-K 文件以及新加坡 GeBIZ 招標。執行一次呼叫合併多個來源的綜合報告:產業全景、構想驗證、政府、金融與公司情報。
適用情境
當助理檢索網路或研究資料、需要判斷內容是否仍然有效以及可信程度時使用;也適合快速取得儲存庫、套件、論文、產品發布、監管文件或合約的帶時間戳快照,而不必自行串接每個來源。
執行需求
可作為 api.freshcontext.dev 的遠端端點經 MCP 遠端橋接使用,也可作為本機 npm 套件(freshcontext-mcp)透過 stdio 執行。本機完整安裝需要 Node.js 20+ 並安裝 Playwright Chromium。未宣告任何 API 金鑰或環境變數。轉接器工具需要網路存取。
安裝前請注意
README 說明此套件已停止開發,0.5.3 為最後一個版本。轉接器工具會存取第三方公開來源,查詢與請求會離開本機。託管端點屬於獨立部署,工具集可能與套件不一致。FreshContext 判斷的是時效性與引用就緒度,而非真實性。YC 轉接器已撤回,呼叫會回傳錯誤。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 FreshContext,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.PrinceGabriel-lgtm%2Ffreshcontext/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "freshcontext": {
      "type": "http",
      "url": "https://api.freshcontext.dev/mcp"
    }
  }
}

README

FreshContext

This package is no longer developed. 0.5.3 is the last release of freshcontext-mcp. It and every earlier release stay available under the MIT License, as is; there will be no further feature releases. For FreshContext services, see https://freshcontext.dev. Security reports: see SECURITY.md.

I asked Claude to help me find a job. It gave me a list of openings. I applied to three of them. Two didn't exist anymore. One had been closed for two years.

Claude had no idea. It presented everything with the same confidence.

That's the problem freshcontext fixes.

This repository is the integrated FreshContext Core/MCP package.

Category: context integrity infrastructure. FreshContext sits between context acquisition and agent action. Its job is to decide whether information entering an AI workflow is still fresh, attributable and coherent enough for the system to rely on. Core is the reusable engine that scores, ranks, explains and turns candidate context into decision-ready context, with signed verdicts recorded in a verifiable ledger. MCP is the first live host interface over that engine — one interface over the methodology, not the product itself.

[npm version] [License: MIT] [MCP Registry]

Live demo: api.freshcontext.dev/demo — same model, same query, two completely different answers. Only the temporal layer changed.

Integrate it into an existing stack: freshcontext.dev/integration — start with one bounded RAG, agent, retrieval, or governance workflow and objective acceptance criteria.


The problem

Large language models retrieve web data semantically. Cosine similarity finds the documents that match a query best — but cosine doesn't know when a document was written.

So a 2022 blog post and a 2026 paper can score nearly identically. The model gets a context window full of stale documents and faithfully summarizes 2022 advice for a 2026 question.

That's not hallucination. That's correct summarization of corrupted retrieval.

Most RAG pipelines rank context correctly semantically but incorrectly temporally.


The layer

FreshContext is context integrity infrastructure for AI agents and retrieval systems. It sits between retrieval and reasoning:

text
candidate context  -> FreshContext Core  -> decision-ready context  -> model / agent / app

FreshContext evaluates freshness, source profile, confidence, utility, provenance material, and failure honesty before context reaches the LLM. The temporal core uses Decay-Adjusted Relevancy:

R_t = R_0 · e^(−λt)
  • R_0 — base semantic relevancy (whatever your retriever already gives you)
  • λ — source-specific decay constant (HN ≈14h half-life, blogs ≈29d, academic papers ≈1.6y)
  • t — hours elapsed since publication
  • R_t — decay-adjusted relevancy at query time

That's the core correction. No model swap. No re-embedding. No re-indexing. The layer drops onto whatever retrieval pipeline you already have.

The layer is the product. The named adapters shipped with this repo demonstrate compatibility across different source classes. The DAR engine, the freshness envelope, Source Profiles, and the FreshContext Specification are the moat.


The standard

Every FreshContext-compatible response wraps content in a structured envelope:

[FRESHCONTEXT]Source: https://github.com/owner/repoPublished: 2024-11-03Retrieved: 2026-03-05T09:19:00ZConfidence: high---... content ...[/FRESHCONTEXT]

When it was retrieved. Where it came from. How confident we are the date is accurate.

The FreshContext Specification v1.2 is published as an open standard under MIT licence. Any tool, agent, or system that wraps retrieved data in this envelope is FreshContext-compatible. → Read the spec · Read the methodology


Architecture boundary

FreshContext Core is the reusable center of the current integrated package. It owns signal normalization, freshness scoring, Source Profiles, decision output, envelope formatting, failure guards, shared types, rank/explain primitives, and the context-conditioned utility primitive.

MCP is the primary reference/interface implementation over Core. Claude Desktop is supported, but not required. The MCP tool surface exposes named reference adapters and a live interface for using the system.

The production Cloudflare Worker now uses Core-backed envelope generation. Worker-specific concerns remain outside Core: MCP transport, runtime guards, KV cache policy, cache metadata injection, JSON parse/replace cache helpers, D1 feeds, cron, rate limiting, and Store/feed scoring/provenance.

See Architecture for the layer boundaries, the package surface, and what is not yet separated.

Core import path

FreshContext Core is also available directly from the current MCP package:

ts
import {  evaluateSignals,  interpretEvaluations,  getSourceProfile,  normalizeSignal,  calculateHaPriV2,} from "freshcontext-mcp/core";

This is a Core subpath export inside freshcontext-mcp, not a standalone freshcontext-core package yet. The root package and freshcontext-mcp binary remain the MCP reference host.


Primary MCP interface

The clearest MCP path is evaluate_context.

It accepts candidate context from any retriever, agent, database, local script, note parser, or adapter output:

json
{  "profile": "academic_research",  "intent": "citation_check",  "signals": [    {      "title": "Example source",      "content": "Candidate context text...",      "source": "https://example.com/source",      "source_type": "arxiv",      "published_at": "2026-05-24T12:00:00.000Z",      "retrieved_at": "2026-05-24T13:00:00.000Z",      "semantic_score": 0.92    }  ]}

FreshContext returns decision-first output:

  • Decision
  • Meaning
  • Action
  • Warnings
  • Source
  • Freshness
  • Rank score
  • Utility
  • Confidence
  • Why

Structured results also include a readable object for humans:

json
{  "decision": "cite_as_primary",  "label": "Cite as primary",  "readable": {    "label": "Primary source",    "summary": "This source is strong enough to use as main evidence.",    "why": [      "Strong semantic match and current freshness for arxiv.",      "source profile academic_research uses lenient date policy",      "intent profile citation_check selected"    ],    "action": "Use this as main evidence while preserving citation and provenance.",    "warnings": [      "FreshContext judges citation readiness and context usefulness; it does not certify truth."    ]  }}

The readable object translates Core decisions into user-facing language. It does not change ranking, decision labels, utility scoring, or source intake. Utility helps explain usefulness for the current question; it remains explanatory and does not control default decision labels or ranking.

FreshContext does not certify truth. It records why context was used, supported, questioned, refreshed, watched, or excluded before it reaches a model.

evaluate_context does not fetch URLs, crawl, scrape, browse, read folders, or call adapters. It only evaluates candidate context the caller provides.

Current boundary: evaluate_context ships in the npm/local stdio MCP server. The hosted Cloudflare Worker MCP endpoint is a separate deployment surface and is verified independently — check /v1/health for its live version and tool count rather than assuming parity with the package. The Worker remains a separate deployment surface, so future package interfaces should be re-verified remotely before being claimed live.

Network Boundary

FreshContext's primary evaluate_context path does not fetch, crawl, scrape, browse, read folders, or call adapters. The MCP package also includes read-only reference adapters that use network access only when those adapter tools are invoked. Supply-chain scanners may therefore report package network access; that applies to the optional adapter surface, not to caller-provided context evaluation.


Advanced Worker/feed surface

Beyond the per-call Core/MCP paths, the production Worker deployment exposes a continuous, decay-scored, deduplicated feed. This is an advanced deployment surface, not the required way to use FreshContext Core:

GET /v1/intel/feed/:profile_id?limit=20&min_rt=0

Every signal is stamped with base_score, rt_score, entropy_level (low / stable / high), ha_pri_sig (Ha-Pri v1 SHA-256 provenance reference), semantic_fingerprint (cross-adapter dedup), and published_at. Ready for direct LLM or agent consumption — no synthesis required.

Production endpoint: https://api.freshcontext.dev


Reference adapters

The repo ships named reference adapters that demonstrate how different source classes can become FreshContext-compatible. Each adapter keeps its own name because it represents a source boundary; the adapter count is operational proof, not the product headline.

Intelligence

AdapterWhat it returns
extract_githubREADME, stars, forks, language, topics, last commit
extract_hackernewsTop stories or search results with scores and timestamps
extract_scholarResearch papers — titles, authors, years, snippets
extract_arxivarXiv papers via official API
extract_redditPosts and community sentiment from any subreddit

Competitive research

AdapterWhat it returns
extract_ycYC company listings by keyword
extract_producthuntRecent launches by topic
search_reposGitHub repos ranked by stars with activity signals
package_trendsnpm and PyPI metadata — version history, release cadence

Market data

AdapterWhat it returns
extract_financeNo-key Stooq quote data — close, OHLC, volume, quote timestamp, source. Up to 5 tickers.
search_jobsRemote job listings from Remotive, RemoteOK, HN "Who is Hiring"

Composites — multiple sources, one call

AdapterSourcesPurpose
extract_landscape6YC + GitHub + HN + Reddit + Product Hunt + npm in parallel
extract_idea_landscape6HN + YC + GitHub + Jobs + npm + Product Hunt — full idea validation
extract_gov_landscape4Gov contracts + HN + GitHub + changelog
extract_finance_landscape5Finance + HN + Reddit + GitHub + changelog
extract_company_landscape5The full picture on any company

Official, regulatory, and procurement sources

AdapterSourceWhat it returns
extract_changelogGitHub Releases / npm / auto-discoverUpdate history from any repo, package, or website
extract_govcontractsUSASpending.govUS federal contract awards — company, amount, agency, period
extract_sec_filingsSEC EDGAR8-K filings — legally mandated material event disclosures
extract_gdeltGDELT ProjectGlobal news intelligence — 100+ languages, 15-min updates
extract_gebizdata.gov.sgSingapore Government procurement tenders — open dataset

Quick start

For Claude Desktop, Codex, npx, global npm, and source-checkout setup, see the concise client setup guide.

Cloud (no install)

Add to your Claude Desktop config and restart:

Mac: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

json
{  "mcpServers": {    "freshcontext": {      "command": "npx",      "args": ["-y", "mcp-remote", "https://api.freshcontext.dev/mcp"]    }  }}

Restart Claude. Done.

Prefer a guided setup? Visit freshcontext.dev — 3 steps, no terminal.

Local (full Playwright)

Requires: Node.js 20+ (nodejs.org)

bash
git clone https://github.com/PrinceGabriel-lgtm/freshcontext-mcpcd freshcontext-mcpnpm installnpx playwright install chromiumnpm run build

Add to Claude Desktop config:

Mac:

json
{  "mcpServers": {    "freshcontext": {      "command": "node",      "args": ["/Users/YOUR_USERNAME/path/to/freshcontext-mcp/dist/server.js"]    }  }}

Windows:

json
{  "mcpServers": {    "freshcontext": {      "command": "node",      "args": ["C:\\Users\\YOUR_USERNAME\\path\\to\\freshcontext-mcp\\dist\\server.js"]    }  }}
Mac troubleshooting

"command not found: node" — Use the full path:

bash
which node  # copy this output, replace "node" in config

Config file doesn't exist:

bash
mkdir -p ~/Library/Application\ Support/Claudetouch ~/Library/Application\ Support/Claude/claude_desktop_config.json

Usage examples

The npm run demo:* commands below are source-checkout workflows for contributors and evaluators using a cloned repository. The published npm package is the MCP server/runtime package and does not include repo-only source examples or tests.

From an installed npm package, the supported runtime entrypoints are npm start and the freshcontext-mcp binary. Repo-only scripts such as tests, demos, smoke checks, and trust scans print a source-checkout notice when their source files are not present.

The Apify Actor entrypoint remains available in the source checkout for separate actor packaging, but it is intentionally not part of the published MCP npm runtime package.

Release trust gate

Run the local release gate before a release, package review, demo, or PR review:

bash
npm run trust:gate

The gate runs the Trust Scanner with repo-map reporting, npm package-boundary inspection, deterministic claim checks, and --fail-on fail. It is local-only, does not publish or deploy, does not send telemetry, and does not replace dedicated security scanners.

Generate review reports when you need a shareable summary:

bash
npm run trust:reportnpm run trust:report:json

To write a Markdown report file explicitly:

bash
npm run trust:report -- --output TRUST_SCAN_REPORT.md

Bring your own source list

FreshContext can evaluate candidate context you provide as a local JSON file:

bash
npm run demo:evaluate:file

To pass a different file:

bash
npm run demo:evaluate:file -- path/to/sources.json

Included examples:

bash
npm run demo:evaluate:file -- examples/sources.academic.example.jsonnpm run demo:evaluate:file -- examples/sources.jobs.example.json

Minimal shape:

json
{  "profile": "academic_research",  "intent": "citation_check",  "signals": [    {      "title": "...",      "content": "...",      "source": "...",      "source_type": "arxiv",      "published_at": "...",      "retrieved_at": "...",      "semantic_score": 0.92    }  ]}

This local demo does not fetch URLs, crawl, or read folders. It evaluates candidate context you provide and returns decision-first output: Decision, Meaning, Action, Warnings, and supporting metrics.

In an MCP client, use evaluate_context when you already have candidate context from another retriever, database, agent, or script:

text
Use evaluate_context with profile "academic_research", intent "citation_check", and these candidate signals: [...]

Use the named reference adapters when you want FreshContext's current MCP package to fetch public source examples for you.

Should I build this idea?

Use extract_idea_landscape with idea "procurement intelligence saas"

Returns funding signal, pain signal, crowding signal, market signal, ecosystem signal, and launch signal — all timestamped.

Full company intelligence in one call:

Use extract_company_landscape with company "Palantir" and ticker "PLTR"

SEC filings + federal contracts + global news + changelog + market data.

Did that company just disclose something material?

Use extract_sec_filings with url "Palantir Technologies"

8-K filings are legally mandated within 4 business days of any material event — CEO change, acquisition, breach, major contract.

Is this dependency still actively maintained?

Use extract_changelog with url "https://github.com/org/repo"

Returns the last 8 releases with exact dates. If the last release was 18 months ago, you'll know before you pin the version.


Deployment & infrastructure

The reference implementation runs on Cloudflare's global edge:

EndpointMethodPurpose
/GETService info + endpoint list
/healthGETLiveness check
/mcpPOSTMCP JSON-RPC transport
/demoGETLive before/after demo (no auth token required)
/briefingGETLatest stored briefing
/v1/intel/feed/:profile_idGETDAR-scored intelligence feed
/watched-queriesGETList all watched queries
/.well-known/freshcontext-signing-keys.jsonGETPublished Ed25519 verification keys (active + retired)
  • D1 database — 18 watched queries running on 6-hour cron with relevancy scoring
  • KV-backed rate limiting — 60 req/min per IP across all edge nodes
  • Defensive valves — clock-skew rejection (5min tolerance), hard floor at R_t<5, lazy decay at read time
  • Provenance — feed signals still carry legacy Ha-Pri v1 SHA-256 provenance references; separately, ledger-backed context verdicts are signed with Ed25519 V4 and independently verifiable
  • Schema migrations — promise-gated, idempotent, run on first request after deploy

Production: https://api.freshcontext.dev


Deployment modes

The engine is deliberately separable from the interface it is reached through. The same Core runs in each of these without a rewrite:

ModeWhat it means
StandaloneFreshContext runs as its own context-integrity service, as it does today.
Embedded subsystemCore runs inside an existing AI, data or security platform, invisible to that platform's users.
SDK / APIIntegrity primitives are consumed programmatically; no MCP involved.
MCP infrastructure layerFreshContext evaluates and governs context around MCP-enabled workflows — the live path in this repo.
Gateway / control-plane componentCore operates at the policy boundary, before context is admitted into agent execution.
White-labelThe engine is surfaced under another product's branding and API.

Only the MCP and standalone modes are exercised in production today. The others are integration seams the architecture already supports, not shipped configurations.


Roadmap

Split three ways so that genuine engineering risk is never filed as optionality. Nothing outside Production core is a live product claim.

Production core — built, running, testable

  • FreshContext Specification v1.2 published (MIT, open standard)
  • DAR engine with source-specific lambda constants
  • Ha-Pri v1 provenance signatures on stored signals
  • Ha-Pri v2 Core helper and deterministic golden vectors
  • Public /v1/verify endpoint — ledger-backed verdict verification, answering for both the legacy HMAC path and Ed25519, and reporting which was used via verification_method
  • Generic MCP evaluate_context tool for caller-provided candidate context
  • Core-backed envelope generation shared by npm/MCP and the Cloudflare Worker
  • Semantic deduplication via fingerprinting
  • Named reference adapters across intelligence, competitive research, market data, and composites
  • Cloudflare Workers deployment — global edge, KV cache, atomic rate limiting
  • Live before/after demo at /demo
  • METHODOLOGY.md — methodology and engineering documentation
  • Published on npm and listed for MCP usage; Apify/feed assets separated from the MCP runtime package
  • Trusted release publishing workflow — manual workflow_dispatch only, OIDC-backed, provenance-enabled, and gated by version/verification checks. One explicit run publishes npm first, verifies it, then publishes the matching manifest to the official MCP Registry with GitHub OIDC
  • Independently verifiable Ed25519 attestation (E-2). Every new verdict row in the ledger is signed FRESHCONTEXT_HA_PRI_V4 with Ed25519. A third party can verify a verdict with no FreshContext account, no API key and no call to FreshContext — using the key document the Worker publishes at /.well-known/freshcontext-signing-keys.json and either verifier shipped in the npm tarball: scripts/verify-offline.mjs (Node, standard library) or scripts/verify_offline.py (Python, no dependencies at all). Written up for the sceptic rather than the maintainer in VERIFYING.md.
  • Signing key fc-2026-09-ceced1ab published and active. Keys are append-only, so a rotation never invalidates a verdict signed under a key that has since been retired.
  • attestation-proof.yml — obtains a live verdict, verifies it with both shipped verifiers, runs tampered-payload and tampered-signature negative controls, and confirms the stored ledger row is V4 rather than only the emitted response block. On demand and daily; every input it uses is public, so it needs no credentials to run.

In flight on the core, not an expansion surface:

  • Ha-Pri v2 Worker/D1 production enforcement for stored signals — the feed rows, which still carry Ha-Pri v1 SHA-256 stamps. This is a separate path from the verdict ledger above: verdicts are V4/Ed25519 today, signals are not. Design document complete; hard tamper enforcement on the signals path is not live.

Expansion surfaces — deliberately open, not built

These are integration seams the architecture supports and the engine does not yet implement. Stated in future tense on purpose.

  • Context safety harness. Policy enforcement before context reaches an agent: pass / warn / refresh / quarantine / block, with evidence attached to each decision. Today evaluate_context emits decisions and warnings; the enforcement state machine does not exist — quarantine and block are not implemented anywhere in the codebase.
  • Enterprise control plane. Dashboard over source health, trust score, context drift and provenance lineage. The verdict ledger is the data contract this would read from; the UI is unbuilt.
  • Observability telemetry. Historical integrity state, incidents, upstream degradation and remediation history.
  • Autonomous remediation. Automatic refresh, source substitution and re-evaluation — closed-loop rather than detection-only.
  • Vertical policy packs. Domain-specific integrity thresholds for regulated workflows.
  • Webhook triggers — push high-entropy signals on threshold

Research frontier — exploration, not commitment

  • GKG upgrade for extract_gdelt — tone scores, goldstein scale, event codes
  • Contradiction detection across concurrent sources

Future work is organized in FreshContext Future Lanes. Roadmap items are not live product claims until implemented and validated.


Contributing

PRs welcome. The highest-value contributions improve the caller-provided context path, decision output, host integrations, and FreshContext-compatible signal quality. New reference adapters are useful when they preserve source boundaries and emit timestamped, failure-honest context — see src/adapters/ for examples and FRESHCONTEXT_SPEC.md for the compatibility contract.

If you're building something FreshContext-compatible, open an issue and we'll add you to the ecosystem list.


Trust and security


License

MIT


Built by Immanuel Gabriel — Namibia 🇳🇦 "The work isn't gone. It's just waiting to be continued."


Also on: MCP Registry · npm

來源:README.md,提交 53ed204

工具

22

evaluate_context

Evaluate caller-provided candidate context and return decision-ready output. This is the primary FreshContext judgment path: it does not fetch, crawl, scrape, browse, read folders, or call adapters.

輸入參數nowintent必填profile必填signals必填

extract_github

Extract real-time data from a GitHub repository — README, stars, forks, last commit, topics. Returns timestamped freshcontext.

輸入參數url必填

extract_hackernews

Extract top stories or search results from Hacker News. The url field accepts an HN/Algolia URL or a plain search query.

輸入參數url必填

extract_scholar

Extract research results (title, authors, year, snippet) from a Google Scholar search URL. Scholar exposes only the publication year, not an exact date, so freshness confidence is capped at medium.

輸入參數url必填

extract_yc

Withdrawn in 0.5.3 pending review of source terms. Returns an error; kept so existing clients get a clear answer.

輸入參數url必填

search_repos

Search GitHub for repositories matching a keyword. Returns top results by stars.

輸入參數query必填

extract_reddit

Extract recent posts from a subreddit (e.g. 'r/MachineLearning') or a Reddit search/listing URL. Returns title, subreddit, author, score, comment count, and post date per result.

輸入參數url必填

extract_producthunt

Extract recent Product Hunt launches by keyword/topic search or a PH URL. Returns name, tagline, votes, comment count, topics, and launch date per result.

輸入參數url必填

extract_finance

No-key stock quote data via Stooq. Accepts comma-separated ticker symbols and returns quote/OHLC/volume observations with timestamps.

輸入參數url必填

search_jobs

Search for real-time job listings with freshness badges on every result. Sources: Remotive + HN Who is Hiring.

輸入參數query必填max_length

extract_landscape

Composite tool. Queries GitHub + HN + npm simultaneously. Returns a unified landscape report with each source's own freshness.

輸入參數topic必填

extract_arxiv

Search arXiv for research papers via the official API. Pass a topic or full arXiv API URL. Returns titles, authors, dates, abstracts.

輸入參數url必填

extract_changelog

Update history for any product. Accepts a GitHub repo URL or an npm package name. Returns version numbers, release dates, and entries.

輸入參數url必填

extract_gdelt

Global news intelligence from GDELT. Monitors news from every country in 100+ languages, updated every 15 minutes. Returns articles with source country, language, date.

輸入參數url必填

extract_gebiz

Singapore Government procurement opportunities (GeBIZ via data.gov.sg). Search by keyword, agency name, or empty for all recent tenders.

輸入參數url必填

extract_govcontracts

US federal contract awards from USASpending.gov. Search by company name (e.g. 'Palantir'), keyword, or NAICS code. Returns amounts, dates, agencies.

輸入參數url必填

extract_sec_filings

SEC 8-K filings via EDGAR full-text search. 8-K = legally mandated material event disclosures (CEO changes, M&A, breaches). Pass company name, ticker, or keyword.

輸入參數url必填

extract_gov_landscape

Composite government intelligence: federal contract awards (USASpending) + dev community awareness (HN) + GitHub repo activity + product release velocity (changelog). 4-source unified report.

輸入參數query必填github_url

extract_finance_landscape

Composite financial intelligence: Stooq quote data + HN sentiment + Reddit discussion + GitHub ecosystem + product changelog. 5-source unified report.

輸入參數tickers必填company_namegithub_query

extract_company_landscape

Most complete single-call company intelligence: SEC 8-K filings + USASpending federal contracts + GDELT global news + product changelog + Stooq quote data. 5 unique sources.

輸入參數tickercompany必填github_url

extract_idea_landscape

Idea validation composite: HN pain signals + YC funded competitors + GitHub crowding + jobs market signal + npm/PyPI ecosystem + Product Hunt launches. 6 sources.

輸入參數idea必填

版本歷史

3
  1. v0.5.3最新Sep 27, 2026
  2. v0.5.2Sep 21, 2026
  3. v0.3.17Sep 16, 2026

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