Disco

Name: Disco
Author: leap-laboratories

@leap-laboratories

Repository

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.