Discovery Engine MCP Server

Find novel, statistically validated patterns in tabular data — hypothesis-free. The discovery engine mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 1 defined tool rather than through you.

What it actually does

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

Adding it to your client

The server ships on PyPI as discovery-engine-api, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.

Its toolset

Everything the assistant can do here goes through one of these:

• Goal — Tool

Configuration

You will need one environment variable: DISCOVERY_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.

Caveats

• It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
• Missing credentials fail quietly in some clients — if no tools show up, check the environment block first.
• MCP clients confirm each tool call by default. Leave that on until you have watched what the discovery engine mcp server does with a few real requests.

When to reach for it

Among the developer tooling options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Discovery Engine's toolset — Goal — is a fair guide to whether it matches your workflow. It is maintained by com.leap-labs; worth a glance at recent repository activity before you build anything load-bearing on it.

This entry was verified against Discovery Engine's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.