Lakelogic Mcp

io.github.LakeLogicPublished by GitHub account LakeLogicv0.1.4Updated Oct 29, 2026

Validate, lint, test-run and explain Open Lakehouse Contracts with LakeLogic, from any AI assistant.

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Server details

Identifier: io.github.LakeLogic/lakelogic-mcp
Transport: STDIO
Runtime: Desktop only
Trust: Verified
Published: Oct 15, 2026

Security note

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The registry verified the GitHub account LakeLogic for this listing. That does not show the account is the product's maker.

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Overview

AI-generated overview

Local MCP server that lets an AI assistant validate, lint, explain and test-run Open Lakehouse Contracts with LakeLogic.

What you can do: Validate contracts against the OLC model and list each error's location and problem
Lint contracts for governance gaps, with a suggested fix per finding
Explain lint rules and look up how parts of a contract are written
Export a contract as an Open Data Contract Standard document
Generate synthetic test data shaped by the contract, including rows that break its rules
Run a contract on a local file with Polars or DuckDB and report passing rows, failing rows and failure reasons
When to use it: Useful when you keep data contracts in a project and want an assistant to check them, understand lint findings, produce sample data and see real pass/fail results instead of guessed YAML.
Requirements: Runs locally over stdio as the PyPI package lakelogic-mcp; uv must be on your PATH. Start the assistant in the project holding the contracts or pass a root folder. No LakeLogic account, API key or environment variables are needed.

Before you install

Tool paths and the domain/system files lint reads must stay inside the project folder, but files a contract names itself are read as written. Running a contract can send notifications or run-log deliveries if the contract configures them. Tool results, including sample rows, failure reasons that may quote your data, and the contract, go into the AI provider's context; sample: 0 removes only the rows, so use generated data for confidential files.

Installation

In SourceWeft

Open Lakelogic Mcp in the dashboard and add it to a workspace.
Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

[LakeLogic MCP: check and test data contracts with your AI assistant. A local MCP server for validating, linting and test-running OLC contracts. Your AI assistant talks to the LakeLogic MCP local server, which uses LakeLogic Core with Polars or DuckDB for contract checks (validate, lint, explain) and test runs (sample data, results, failure reasons). No LakeLogic account required.]

LakeLogic MCP

mcp-name: io.github.LakeLogic/lakelogic-mcp

Help AI assistants check and test data contracts locally.

Validate Open Lakehouse Contracts (OLC), find governance gaps, explain rules and generate sample data. Test-run contracts with LakeLogic Core using Polars or DuckDB, then inspect accepted rows, failed rows and the reasons they failed.

Works with MCP-compatible assistants — Claude Code, Claude Desktop, Cursor and others. No LakeLogic account required.

OLC defines the contract. Core executes it. MCP gives your assistant access to those tools.

📖 Documentation: https://mcp.lakelogic.io/

Example

"Check orders.yaml, explain any findings, generate sample data and test it locally."

The assistant validates the contract, lints it, explains each finding, writes test data shaped by the contract, runs it, and reports which rows passed and why the others failed — real results from LakeLogic, not plausible-looking YAML.

Tools

Tool	Answers
`validate_contract`	Does it match the OLC model? Each error's location and problem
`lint_contract`	Does it pass the lint checks? Governance findings, each with its fix
`explain_rule`	Why does a lint rule exist? What it checks, why it matters, a failing and a fixed contract
`contract_reference`	How is one part of a contract written? (`anatomy`, `quality`, `materialization`, …)
`export_odcs`	The contract as an Open Data Contract Standard document
`generate_test_data`	Synthetic data shaped by the contract, with a share of rows that deliberately break its rules
`run_contract`	How many rows pass, how many fail, and why (Polars or DuckDB, on a local file)

Details and inputs: Tools. A first run, start to finish: Walkthrough.

Install

All clients need uv on your PATH. Start your assistant in the project that holds your contracts (or pass --root <folder>).

Claude Code — as a plugin:

or directly: claude mcp add lakelogic -- uvx lakelogic-mcp

Cursor: Add to Cursor

Claude Desktop, VS Code and other MCP clients — add to the client's MCP settings:

json

{  "mcpServers": {    "lakelogic": {      "command": "uvx",      "args": ["lakelogic-mcp", "--root", "/path/to/your/contracts/project"]    }  }}

{  "mcpServers": {    "lakelogic": {      "command": "uvx",      "args": ["lakelogic-mcp", "--root", "/path/to/your/contracts/project"]    }  }}

It is listed in the official MCP Registry as io.github.LakeLogic/lakelogic-mcp.

Safety and privacy

Local. The server talks to your client over stdio; it opens no network connection itself, needs no account and sends no telemetry. (Installing with uvx downloads the package, as any install does.)
Tool paths stay in your project. Every path you give a tool, and the _domain.yaml / _system.yaml that lint takes into account, must be inside the project folder. Files a contract itself names (a links: dataset, for example) are read by Core as written.
Running a contract does what the contract configures. If a contract declares notifications or run-log delivery, LakeLogic Core may send them during run_contract. Nothing is written to the contract's target.
Results reach your AI provider. Tool results go into the assistant's context: quarantined sample rows, failure reasons (which can quote your data) and the contract itself. sample: 0 removes only the rows, so test with generated data if a file holds personal or confidential data.

More: Safety and privacy.

Contributing

To work on the server from a checkout, see Development.

Source: README.md at commit ea85927

Tools

SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

v0.1.4LatestOct 29, 2026
v0.1.3Oct 24, 2026
v0.1.2Oct 18, 2026
v0.1.1Oct 15, 2026

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