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Disco

@leap-laboratories

Disco について

Discovery Engine — find novel, statistically validated patterns in tabular data

基本情報

カテゴリ

その他

ライセンス

MIT

ランタイム

python

トランスポート

stdio

公開者

leap-laboratories

投稿者

Jessica Rumbelow

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "discovery-engine": {
      "url": "https://disco.leap-labs.com/mcp",
      "headers": {
        "Authorization": "Bearer disco_..."
      }
    }
  }
}

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概要

What is Disco?

Disco is an MCP server and Python SDK that finds novel, statistically validated patterns in tabular data — feature interactions, subgroup effects, and conditional relationships — without requiring hypotheses. It validates each pattern on a hold‑out set, applies FDR correction, and checks results against academic literature.

How to use Disco?

Install with pip install discovery-engine-api, obtain an API key via the signup API or the developer dashboard, then create an Engine instance and call await engine.discover(file="data.csv", target_column="outcome"). For MCP, configure the server with "url": "https://disco.leap-labs.com/mcp" and set DISCOVERY_API_KEY as an environment variable.

Key features of Disco

  • Unbiased, hypothesis‑free pattern discovery from tabular data
  • Each pattern is validated on a hold‑out set with FDR correction
  • Novelty classification against academic literature
  • Structured output with conditions, effect sizes, p‑values, and citations
  • Interactive web report for every analysis
  • Public runs are free; private runs cost credits

Use cases of Disco

  • Identify unknown feature interactions in clinical or biomedical datasets
  • Discover subgroup effects in customer or operational data
  • Find novel patterns in scientific research without pre‑defined hypotheses
  • Understand which combinations of conditions drive a target outcome

FAQ from Disco

What data formats does Disco support?

CSV, TSV, Excel (.xlsx), JSON, Parquet, ARFF, and Feather. Maximum file size is 5 GB.

How much does Disco cost?

Public runs are free (results and data are published). Private runs cost credits: free tier gives 10 credits/month, Researcher $49/month (500 credits), Team $199/month (2000 credits), individual credits $0.10 each.

How is Disco different from standard data analysis tools?

Disco finds patterns that tools like pandas, AutoML, or LLMs miss. It does not start with a question but discovers statistically validated interactions and subgroup effects from the data itself.

Can I use Disco without an API key?

No. You must sign up via email (no password required) or the developer dashboard to get an API key. Public runs do not require a payment method.

How long does a typical analysis take?

A few minutes. The discover() method polls automatically and logs progress (queue position, ETA, pipeline step).

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