Disco

com.leap-labs由 leap-labs.com 發布官方發布者v1.0.1更新於 Oct 24, 2026

Find novel, statistically validated patterns in tabular data — hypothesis-free.

已驗證Streamable HTTP可網頁執行金融資料與分析

概覽

AI 產生的概覽

Disco 是一個遠端 MCP 伺服器，對表格式資料執行統計探索流程，找出經過驗證並對照文獻的模式，供助理進一步解讀。

可以做什麼
透過連結、路徑或內容上傳表格式資料集，針對指定目標欄位執行探索分析，再查詢進度並取得包含條件、p 值、新穎性評分與引用的模式結果。執行前估算所需點數，並檢視可用方案與價格。查詢帳戶方案、點數餘額與付款方式狀態。透過電子郵件驗證碼註冊或登入以取得 API 金鑰。綁定 Stripe 付款方式、購買點數包並變更訂閱方案。
適用情境
適合想知道哪些變數與某個結果有關、懷疑存在尚未檢驗的交互作用或子群效應，或面對一張平面資料表不知從何下手的情況。它不用於彙總統計、圖表、篩選或 SQL 查詢，這些應交給其他工具。
執行需求
遠端 streamable HTTP 端點，無需本機安裝。需要 Disco API 金鑰，以 Authorization bearer 權杖形式傳送（格式 disco_...），可透過註冊或登入工具或開發者頁面取得。需要能連線至 Disco 服務的網路。私有分析會消耗點數，免費方案每月含 10 點數。
安裝前請注意
私有分析會消耗點數，且此伺服器可綁定付款方式、以每點數 0.10 美元購買點數包、開通或變更付費訂閱（每月 49 或 199 美元），會產生實際支出。公開執行免費，但資料與報告會被公開，私有資料應使用付費的私有模式。上傳的資料集會傳送到第三方服務，API 金鑰屬於憑證。註冊與登入會寄送電子郵件驗證碼。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Disco，將其新增到工作區。
  2. 連接 Authorization token 憑證。它們會被私密儲存，絕不會在公開頁面上展示。
  3. 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/com.leap-labs%2Fdiscovery-engine/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": {
    "disco": {
      "type": "http",
      "url": "https://disco.leap-labs.com/mcp",
      "headers": {
        "Authorization": "Bearer <YOUR_TOKEN>"
      }
    }
  }
}

README

Disco

Find novel, statistically validated patterns in tabular data — feature interactions, subgroup effects, and conditional relationships that humans and agents miss.

[PyPI] [License: MIT]

Made by Leap Laboratories.

What it actually does

Most data analysis starts with a question. Disco starts with the data.

Without biases or assumptions, it finds combinations of feature conditions that significantly shift your target column — things like "patients aged 45–65 with low HDL and high CRP have 3× the readmission rate" — without you needing to hypothesise that interaction first.

Each pattern is:

  • Validated on a hold-out set — increases the chance of generalisation
  • FDR-corrected — p-values included, adjusted for multiple testing
  • Checked against academic literature — to help you understand what you've found, and identify if it is novel.

The output is structured: conditions, effect sizes, p-values, citations, and a novelty classification for every pattern found.

Use it when: "which variables are most important with respect to X", "are there patterns we're missing?", "I don't know where to start with this data", "I need to understand how A and B affect C".

Not for: summary statistics, visualisation, filtering, SQL queries — use pandas for those

Quickstart

bash
pip install discovery-engine-api

Get an API key:

bash
# Step 1: request verification code (no password, no card)curl -X POST https://disco.leap-labs.com/api/signup \  -H "Content-Type: application/json" \  -d '{"email": "[email protected]"}'
# Step 2: submit code from email → get keycurl -X POST https://disco.leap-labs.com/api/signup/verify \  -H "Content-Type: application/json" \  -d '{"email": "[email protected]", "code": "123456"}'# → {"key": "disco_...", "credits": 10, "tier": "free_tier"}

Or create a key at disco.leap-labs.com/developers.

Run your first analysis:

python
from discovery import Engine
engine = Engine(api_key="disco_...")result = await engine.discover(    file="data.csv",    target_column="outcome",)
for pattern in result.patterns:    if pattern.p_value < 0.05 and pattern.novelty_type == "novel":        print(f"{pattern.description} (p={pattern.p_value:.4f})")
print(f"Explore: {result.report_url}")

Runs take a few minutes. discover() polls automatically and logs progress — queue position, estimated wait, current pipeline step, and ETA. For background runs, see Running asynchronously.

→ Full Python SDK reference · Example notebook

What you get back

Each Pattern in result.patterns looks like this (real output from a crop yield dataset):

python
Pattern(    description="When humidity is between 72–89% AND wind speed is below 12 km/h, "                "crop yield increases by 34% above the dataset average",    conditions=[        {"type": "continuous", "feature": "humidity_pct",         "min_value": 72.0, "max_value": 89.0},        {"type": "continuous", "feature": "wind_speed_kmh",         "min_value": 0.0, "max_value": 12.0},    ],    p_value=0.003,              # FDR-corrected    novelty_type="novel",    novelty_explanation="Published studies examine humidity and wind speed as independent "                        "predictors, but this interaction effect — where low wind amplifies "                        "the benefit of high humidity within a specific range — has not been "                        "reported in the literature.",    citations=[        {"title": "Effects of relative humidity on cereal crop productivity",         "authors": ["Zhang, L.", "Wang, H."], "year": "2021",         "journal": "Journal of Agricultural Science"},    ],    target_change_direction="max",    abs_target_change=0.34,     # 34% increase    support_count=847,          # rows matching this pattern    support_percentage=16.9,)

Key things to notice:

  • Patterns are combinations of conditions — humidity AND wind speed together, not just "more humidity is better"
  • Specific thresholds — 72–89%, not a vague correlation
  • Novel vs confirmatory — every pattern is classified; confirmatory ones validate known science, novel ones are what you came for
  • Citations — shows what IS known, so you can see what's genuinely new
  • report_url links to an interactive web report with all patterns visualised

The result.summary gives an LLM-generated narrative overview:

python
result.summary.overview# "Disco identified 14 statistically significant patterns. 5 are novel.#  The strongest driver is a previously unreported interaction between humidity#  and wind speed at specific thresholds."
result.summary.key_insights# ["Humidity × low wind speed at 72–89% humidity produces a 34% yield increase — novel.",#  "Soil nitrogen above 45 mg/kg shows diminishing returns when phosphorus is below 12 mg/kg.",#  ...]

How it works

Disco is a pipeline, not prompt engineering over data. It:

  1. Trains machine learning models on a subset of your data
  2. Uses interpretability techniques to extract learned patterns
  3. Validates every pattern on the held-out data with FDR correction (Benjamini-Hochberg)
  4. Checks surviving patterns against academic literature via semantic search

You cannot replicate this by writing pandas code or asking an LLM to look at a CSV. It finds structure that hypothesis-driven analysis misses because it doesn't start with hypotheses.

Preparing your data

Before running, exclude columns that would produce meaningless findings. Disco finds statistically real patterns — but if the input includes columns that are definitionally related to the target, the patterns will be tautological.

Exclude:

  1. Identifiers — row IDs, UUIDs, patient IDs, sample codes
  2. Data leakage — the target renamed or reformatted (e.g., diagnosis_text when the target is diagnosis_code)
  3. Tautological columns — alternative encodings of the same construct as the target. If target is serious, then serious_outcome, not_serious, death are all part of the same classification. If target is profit, then revenue and cost together compose it. If target is a survey index, the sub-items are tautological.

Full guidance with examples: SKILL.md

Parameters

python
await engine.discover(    file="data.csv",           # path, Path, or pd.DataFrame    target_column="outcome",   # column to predict/explain    analysis_depth=2,          # 2=default, higher=deeper analysis, lower = faster and cheaper    visibility="public",       # "public" (always free, data and report is published) or "private" (costs credits)    column_descriptions={      # improves pattern explanations and literature context        "bmi": "Body mass index",        "hdl": "HDL cholesterol in mg/dL",    },    excluded_columns=["id", "timestamp"],  # see "Preparing your data" above    use_llms=False,                        # Defaults to False. If True, runs are slower and more expensive, but you get smarter pre-processing, summary page, literature context and novelty assessment. Public runs always use LLMs.    title="My dataset",    description="...", # improves pattern explanations and literature context)

Public runs are free but results are published. Set visibility="private" for private data — this costs credits.

Running asynchronously

Runs take a few minutes. For agent workflows or scripts that do other work in parallel:

python
# Submit without waitingrun = await engine.run_async(file="data.csv", target_column="outcome", wait=False)print(f"Submitted {run.run_id}, continuing...")
# ... do other things ...
result = await engine.wait_for_completion(run.run_id, timeout=1800)

For synchronous scripts and Jupyter notebooks:

python
result = engine.run(file="data.csv", target_column="outcome", wait=True)# or: pip install discovery-engine-api[jupyter] for notebook compatibility

MCP server

Disco is available as an MCP server — no local install required.

json
{  "mcpServers": {    "discovery-engine": {      "url": "https://disco.leap-labs.com/mcp",      "env": { "DISCOVERY_API_KEY": "disco_..." }    }  }}

Tools: discovery_list_plans, discovery_estimate, discovery_upload, discovery_analyze, discovery_status, discovery_get_results, discovery_account, discovery_signup, discovery_signup_verify, discovery_login, discovery_login_verify, discovery_add_payment_method, discovery_subscribe, discovery_purchase_credits.

→ Full agent skill file

Pricing

Cost
Public runsFree — results and data are published
Private runsCredits vary by file size and configuration — use engine.estimate()
Free tier10 credits/month, no card required
Researcher$49/month — 500 credits
Team$199/month — 2000 credits
Credits$0.10 per credit

Estimate before running:

python
estimate = await engine.estimate(file_size_mb=10.5, num_columns=25, analysis_depth=2, visibility="private")# estimate["cost"]["credits"] → 55# estimate["account"]["sufficient"] → True/False

Account management is fully programmatic — attach payment methods, subscribe to plans, and purchase credits via the SDK or REST API. See Python SDK reference or SKILL.md.

Expected data format

Disco expects a flat table — columns for features, rows for samples.

| patient_id | age | bmi  | smoker | outcome ||------------|-----|------|--------|---------|| 001        | 52  | 28.3 | yes    | 1       || 002        | 34  | 22.1 | no     | 0       || ...        | ... | ...  | ...    | ...     |
  • One row per observation — a patient, a sample, a transaction, a measurement, etc.
  • One column per feature — numeric, categorical, datetime, or free text are all fine
  • One target column — the outcome you want to understand. Must have at least 2 distinct values.
  • Missing values are OK — Disco handles them automatically. Don't drop rows or impute beforehand.
  • No pivoting needed — if your data is already in a flat table, it's ready to go

Supported formats: CSV, TSV, Excel (.xlsx), JSON, Parquet, ARFF, Feather. Max 5 GB.

Not supported: images, raw text documents, nested/hierarchical JSON, multi-sheet Excel (use the first sheet or export to CSV)

Compared to other tools

GoalTool
Summary statistics, data qualityydata-profiling, sweetviz
Predictive modelAutoML (auto-sklearn, TPOT, H2O)
Quick correlationspandas, seaborn
Answer a specific question about dataChatGPT, Claude
Find what you don't know to look forDisco

Disco isn't a replacement for EDA or AutoML — it finds the patterns those tools miss. We tested 18 data analysis tools on a dataset with known ground-truth patterns. Most confidently reported wrong results. Disco was the only one that found every pattern.

Links

來源：README.md，提交 70cc9b9

工具

14

discovery_list_plans

List Plans

List available Disco plans with pricing. No authentication required. Returns all available subscription tiers with credit allowances and pricing. Use this to help users choose a plan.

無需輸入參數。

discovery_estimate

Estimate Analysis Cost

Estimate the credits required to run a Disco analysis. Returns `required_credits` for public (always 0) and private, with private split by whether LLMs are enabled (use_llms=False is faster, use_llms=True adds smarter preprocessing, literature context and a written summary). Also returns per-visibility depth caps and accepted file formats. No authentication required — when an API key is supplied, also returns the caller's available credits. Call this before discovery_analyze whenever cost or feasibility is unclear. Args: file_size_mb: Size of the dataset in megabytes. num_columns: Number of columns in the dataset. analysis_depth: Search depth (1=fast, higher=deeper). Used to compute the private-run cost. Default 2. api_key: Disco API key (disco_...). Optional. When provided, the response includes `account.available_credits`.

輸入參數api_keynum_columns必填file_size_mb必填analysis_depth

discovery_upload

Upload Dataset

Upload a dataset file and return a file reference for use with discovery_analyze. Call this before discovery_analyze. Pass the returned result directly to discovery_analyze as the file_ref argument. Provide exactly one of: file_url, file_path, or file_content. Args: file_url: A publicly accessible http/https URL. The server downloads it directly. Best option for remote datasets. file_path: Absolute path to a local file. Only works when running the MCP server locally (not the hosted version). Streams the file directly — no size limit. file_content: File contents, base64-encoded. For small files when a URL or path isn't available. Limited by the model's context window. file_name: Filename with extension (e.g. "data.csv"), for format detection. Only used with file_content. Default: "data.csv". api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

輸入參數api_keyfile_urlfile_namefile_pathfile_content

discovery_analyze

Run Discovery Analysis

Run Disco on tabular data to find novel, statistically validated patterns. This is NOT another data analyst — it's a discovery pipeline that systematically searches for feature interactions, subgroup effects, and conditional relationships nobody thought to look for, then validates each on hold-out data with FDR-corrected p-values and checks novelty against academic literature. This is a long-running operation. Returns a run_id immediately. Use discovery_status to poll and discovery_get_results to fetch completed results. Use this when you need to go beyond answering questions about data and start finding things nobody thought to ask. Do NOT use this for summary statistics, visualization, or SQL queries. Public runs are free but results are published. Private runs cost credits. Call discovery_estimate first to check cost. Private report URLs require sign-in — tell the user to sign in at the dashboard with the same email address used to create the account (email code, no password needed). Call discovery_upload first to upload your file, then pass the returned file_ref here. Args: target_column: The column to analyze — what drives it, beyond what's obvious. file_ref: The file reference returned by discovery_upload. analysis_depth: Search depth (1=fast, higher=deeper). Default 1. visibility: "public" (free) or "private" (costs credits). Default "public". title: Optional title for the analysis. description: Optional description of the dataset. excluded_columns: Optional JSON array of column names to exclude from analysis. column_descriptions: Optional JSON object mapping column names to descriptions. Significantly improves pattern explanations — always provide if column names are non-obvious (e.g. {"col_7": "patient age", "feat_a": "blood pressure"}). author: Optional author name for the report. source_url: Optional source URL

輸入參數titleauthorapi_keyfile_refuse_llmssource_urlvisibilitydescriptiontarget_column必填analysis_depthexcluded_columnscolumn_descriptions

discovery_status

Check Analysis Status

Check the status of a Disco run. Returns current status and progress details: - status: "pending" | "processing" | "completed" | "failed" - job_status: underlying job queue status - queue_position: position in queue when pending (1 = next up) - current_step: active pipeline step (preprocessing, training, interpreting, reporting) - estimated_wait_seconds: estimated queue wait time in seconds (pending only) Poll this after calling discovery_analyze. Use discovery_get_results to fetch full results once status is "completed". Args: run_id: The run ID returned by discovery_analyze. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

輸入參數run_id必填api_key

discovery_get_results

Get Analysis Results

Fetch the full results of a completed Disco run. Returns discovered patterns (with conditions, p-values, novelty scores, citations), feature importance scores, a summary with key insights, column statistics, and suggestions for what to explore next. The response includes a `dashboard_urls` object with direct links to each page of the interactive report — use these to direct the user to the most relevant view: - **summary**: AI-generated overview with key insights, novel findings, and plain-language explanation of the most important findings - **patterns**: Full list of discovered patterns with conditions, effect sizes, p-values, novelty scores, citations, and interactive visualizations - **features**: Feature importances, feature statistics and distribution plots, and correlation matrix - **territory**: Interactive 3D map showing how patterns select different regions of the data Only call this after discovery_status returns "completed". Args: run_id: The run ID returned by discovery_analyze. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

輸入參數run_id必填api_key

discovery_account

Get Account Info

Check your Disco account status. Returns current plan, available credits (subscription + purchased), and payment method status. Use this to verify you have sufficient credits before running a private analysis. Args: api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

輸入參數api_key

discovery_signup

Sign Up

Create a Disco account and get an API key. Provide an email address to start the signup flow. If email verification is required, returns {"status": "verification_required"} — the user will receive a 6-digit code by email, then call discovery_signup_verify to complete signup and receive the API key. The free tier (10 credits/month, unlimited public runs) is active immediately. No authentication required. Returns 409 if the email is already registered. Args: email: Email address for the new account. name: Display name (optional — defaults to email local part).

輸入參數nameemail必填

discovery_signup_verify

Verify Sign Up

Complete Disco signup using an email verification code. Call this after discovery_signup returns {"status": "verification_required"}. The user receives a 6-digit code by email — pass it here along with the same email address used in discovery_signup. Returns an API key on success. Args: email: Email address used in the discovery_signup call. code: 6-digit verification code from the email.

輸入參數code必填email必填

discovery_login

Log In

Get a new API key for an existing Disco account. Sends a 6-digit verification code to the email address. Call discovery_login_verify with the code to receive a new API key. Use this when you need an API key for an account that already exists (e.g. the key was lost or this is a new agent session). Returns 404 if no account exists with this email — use discovery_signup instead. Args: email: Email address of the existing account.

輸入參數email必填

discovery_login_verify

Verify Log In

Complete login and receive a new API key. Call this after discovery_login returns {"status": "verification_required"}. The user receives a 6-digit code by email — pass it here along with the same email address. Returns a new API key on success. Args: email: Email address used in the discovery_login call. code: 6-digit verification code from the email.

輸入參數code必填email必填

discovery_add_payment_method

Add Payment Method

Attach a Stripe payment method to your Disco account. The payment method must be tokenized via Stripe's API first — card details never touch Disco's servers. Required before purchasing credits or subscribing to a paid plan. To tokenize a card, call Stripe's API directly: POST https://api.stripe.com/v1/payment_methods with the stripe_publishable_key from your account info. Args: payment_method_id: Stripe payment method ID (pm_...) from Stripe's API. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

輸入參數api_keypayment_method_id必填

discovery_purchase_credits

Purchase Credits

Purchase Disco credit packs using a stored payment method. Credits cost $0.10 each, sold in packs of 100 ($10/pack). Credits are used for private analyses (public analyses are free). Requires a payment method on file — use discovery_add_payment_method first. Args: packs: Number of 100-credit packs to purchase. Default 1. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

輸入參數packsapi_key

discovery_subscribe

Subscribe to Plan

Subscribe to or change your Disco plan. Available plans: - "free_tier": Explorer — free, 10 credits/month - "tier_1": Researcher — $49/month, 500 credits/month - "tier_2": Team — $199/month, 2000 credits/month Paid plans require a payment method on file. Credits roll over on paid plans. Args: plan: Plan tier ID ("free_tier", "tier_1", or "tier_2"). api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

輸入參數plan必填api_key

版本歷史

1
  1. v1.0.1最新Sep 16, 2026

評分

已滿足適用檢查項的 100%

該評分衡量此收錄條目的完整程度，依據註冊中心條目、端點與公開來源計算得出。它不是對伺服器本身的評價，也不表示伺服器安全或目前可用：可連通性是帶日期的檢查結果，評分也不包含安裝量或使用者評價。

  • 近 30 天內可連通必需25 分
  • 已宣告認證方式必需10 分
  • 已列出工具必需15 分
  • 描述必需15 分
  • 圖示10 分
  • 文件15 分
  • 授權條款10 分