mcp-ml-lab

Let AI agents run real ML experiments end-to-end.

An MCP server that gives Claude (or any MCP-aware AI agent) the ability to profile a CSV, define an ML task, tune XGBoost and LightGBM with Optuna, and produce a markdown report with feature importance — all from natural language.

Why this exists

The existing ML-related MCP servers wrap MLflow, ZenML, or Weights & Biases and expose them as read-only — agents can browse experiment history but can't actually run anything. mcp-ml-lab fills the gap: it lets agents execute the full experimentation loop from a user's natural-language request.

A user typing "train a model on titanic.csv to predict survival" should not need to know what XGBoost is, what cross-validation is, or how to write a hyperparameter search. The agent handles all of that — mcp-ml-lab is the tools layer that makes it possible.

Quick start

pip install mcp-ml-lab

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "ml-lab": {
      "command": "mcp-ml-lab"
    }
  }
}

Restart Claude Desktop. The five tools below are now available.

Example queries

Try these in Claude Desktop with mcp-ml-lab connected:

"Profile this CSV and tell me if there's class imbalance"
"Compare XGBoost and LightGBM on titanic.csv with 60 seconds of tuning"
"Show me the top 10 features the winning model used"
"How did my last three experiments on the wine dataset compare?"

Tools

Tool	What it does
`inspect_data`	Profile a CSV — shape, dtypes, nulls, summary stats, class balance
`define_task`	Register an ML task (CSV + target + classification/regression)
`run_experiment`	Train one or more models, optionally tuning with Optuna
`get_results`	Markdown report with metrics, hyperparameters, feature importance
`compare_runs`	Side-by-side comparison of multiple experiments

Each tool's full signature is in its docstring; they self-document to the LLM.

How it works

Claude Desktop  ───MCP/stdio───  mcp-ml-lab server
                                  │
                                  ├── data.py        CSV loading, schema inference, preprocessor
                                  ├── trainers/      Pluggable XGBoost + LightGBM adapters
                                  ├── search.py      Stratified CV + Optuna TPE tuning
                                  ├── metrics.py     Accuracy, F1, AUC, log loss
                                  ├── storage.py     SQLite via SQLAlchemy 2.0
                                  └── reporting.py   Markdown report generation

All experiments and trials are persisted to ~/.mcp-ml-lab/store.db so an agent can refer back to runs across sessions.

Full design notes in ARCHITECTURE.md.

Roadmap

v0.1.0 ships classification with XGBoost and LightGBM. Planned for v0.2.0+:

Regression tasks
Time series forecasting (sktime / darts integration)
Deep learning baselines (pytorch-tabular)
Optuna multi-objective search (accuracy × latency × model size)
Persisted model artifacts with Docker reproducibility
Permutation feature importance (bias-free alternative to gain importance)
Notebook export — emit a Jupyter notebook that reproduces the winning run

Issues and PRs welcome.

Development

git clone https://github.com/rohithraju-ops/mcp-ml-lab.git
cd mcp-ml-lab
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest -v

Local debugging is easiest with the MCP Inspector:

npx @modelcontextprotocol/inspector mcp-ml-lab

License

MIT.

mcp ml lab