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Run end-to-end ML experiments from natural language (XGBoost, LightGBM, Optuna).
Run end-to-end ML experiments from natural language (XGBoost, LightGBM, Optuna).
Valid MCP server (1 strong, 4 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-rohithraju-ops-mcp-ml-lab": {
"args": [
"mcp-ml-lab"
],
"command": "uvx"
}
}
}From the project's GitHub README.
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.
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.
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.
Try these in Claude Desktop with mcp-ml-lab connected:
| 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.
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.
v0.1.0 ships classification with XGBoost and LightGBM. Planned for v0.2.0+:
Issues and PRs welcome.
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
MIT.
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