MCP Ml Lab

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

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.