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MCP Ml Lab

Run CSV-based classification experiments with XGBoost or LightGBM, Optuna tuning, persisted runs, reports, and comparisons.

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Install MCP Ml Lab

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "mcp-ml-lab": {
      "command": "uvx",
      "args": [
        "mcp-ml-lab"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

uvx mcp-ml-lab

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 2d ago.

Overview

The mcp-ml-lab MCP server lets an MCP-aware agent inspect CSV data, define an ML task, train XGBoost or LightGBM models, and review experiment results. It communicates over MCP/stdio and stores experiments and tuning trials in a local SQLite database. Use it when you want an agent to manage a tabular classification workflow from natural-language requests, including optional Optuna tuning and run comparison.

Use cases

  • Profile a CSV for schema issues and class imbalance
  • Define a classification task from a target column
  • Train XGBoost and LightGBM models with optional tuning
  • Review metrics, hyperparameters, and feature importance
  • Compare results from multiple experiments

Key features

  • CSV profiling with schema and class-balance details
  • XGBoost and LightGBM training
  • Optuna TPE hyperparameter tuning
  • Persisted experiments in local SQLite
  • Markdown result reports
  • Side-by-side run comparison

Tools5

What MCP Ml Lab lets your agent do. Expand a tool for its parameters.

Self-reported~89 tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 1d ago

Recent health check succeeded.

5/5 checks healthy over the last 45d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 9d ago via OSV.dev · mcp-ml-lab (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

Last commit4mo agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Tools exposed5Callable tools this server registers over MCP.
Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.

How MCP Ml Lab works

What mcp-ml-lab MCP server does

The mcp-ml-lab MCP server connects an MCP-aware AI agent to a local machine-learning experimentation workflow for CSV datasets. It supports data profiling, task registration, model training, result reporting, and comparison of previous runs. The README identifies classification with XGBoost and LightGBM as the functionality shipped in version 0.1.0.

An agent can inspect a dataset before training, identify its schema and missing values, register the target column and task type, then launch one or more experiments. The workflow is intended for tabular data and is controlled through MCP tools rather than direct command-line arguments or notebook code.

How it works

The server uses MCP over standard input and output. Its internal components load CSV files, infer schemas, prepare data, train models through XGBoost and LightGBM adapters, run cross-validation, and generate reports. Optuna can perform TPE-based hyperparameter searches when tuning is requested.

Five tools expose the workflow:

  • inspect_data returns dataset shape, data types, null information, summary statistics, and class-balance details.
  • define_task records a CSV, target column, and task type for later experiments.
  • run_experiment trains one or more supported models and can invoke Optuna tuning.
  • get_results produces a Markdown report containing metrics, hyperparameters, and feature importance.
  • compare_runs presents multiple experiments side by side.

The listed metrics include accuracy, F1, AUC, and log loss. Experiments and tuning trials are saved in ~/.mcp-ml-lab/store.db, allowing later requests to refer to earlier runs across sessions.

Setup and configuration

Install the package from PyPI with:

Terminal
pip install mcp-ml-lab

For Claude Desktop, add an ml-lab entry whose command is mcp-ml-lab to the client’s MCP server configuration, then restart the application. The setup uses the executable installed by the package and does not document required environment variables or additional configuration values.

The repository also documents an editable development setup using a Python virtual environment, pip install -e ".[dev]", and pytest -v. MCP Inspector can be used for local debugging by launching the installed command through npx @modelcontextprotocol/inspector mcp-ml-lab.

Limitations and notes

The mcp-ml-lab MCP server is documented as shipping classification support with XGBoost and LightGBM in version 0.1.0. Regression, time-series forecasting, deep-learning baselines, multi-objective Optuna searches, model artifact persistence, permutation importance, and notebook export are listed as roadmap items rather than current capabilities.

Feature importance in the current report is available as part of run results; the planned permutation-importance feature is separate from that existing output.

Read the full README View source on GitHub

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Frequently Asked Questions about MCP Ml Lab

mcp-ml-lab is an MCP server that connects AI agents to local CSV-based machine-learning experiments. Its main tools inspect data, define tasks, train XGBoost or LightGBM models with optional Optuna tuning, generate Markdown results with metrics and feature importance, and compare multiple experiment runs stored across sessions.

Install it with `pip install mcp-ml-lab`. The package provides the `mcp-ml-lab` executable, which you can register as an MCP server command in a compatible client.

Add an MCP server entry with `"command": "mcp-ml-lab"` to Claude Desktop’s configuration, then restart Claude Desktop. The README gives the macOS configuration path and names the server entry `ml-lab`.

It supports XGBoost and LightGBM for the classification functionality documented as shipped in version 0.1.0. Optuna can optionally tune their hyperparameters during an experiment.

It stores experiments and tuning trials in `~/.mcp-ml-lab/store.db`, using SQLite. This persistence lets an agent refer to earlier runs across sessions.

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