Mcp Ml Lab
If you want Claude to actually run ML experiments instead of just browsing past results, this is the missing piece. It exposes five tools that let agents profile CSVs, define classification tasks, tune XGBoost and LightGBM models with Optuna, and generate markdown reports with feature importance. Everything persists to SQLite so the agent can compare runs across sessions. You tell Claude "train a model on titanic.csv to predict survival" and it handles the full loop: preprocessing, stratified cross validation, hyperparameter search, evaluation. Regression and time series forecasting are on the roadmap. Useful when you want to prototype models conversationally without writing boilerplate.
- tools
- 5
- transport
- STDIO
- last updated
- May 29, 2026
Tools
5 toolsPublic tool metadata for what this MCP can expose to an agent.
inspect_dataProfile a CSV — shape, dtypes, nulls, summary stats, class balance
Profile a CSV — shape, dtypes, nulls, summary stats, class balance
No parameter schema in public metadata yet.
define_taskRegister an ML task (CSV + target + classification/regression)
Register an ML task (CSV + target + classification/regression)
No parameter schema in public metadata yet.
run_experimentTrain one or more models, optionally tuning with Optuna
Train one or more models, optionally tuning with Optuna
No parameter schema in public metadata yet.
get_resultsMarkdown report with metrics, hyperparameters, feature importance
Markdown report with metrics, hyperparameters, feature importance
No parameter schema in public metadata yet.
compare_runsSide-by-side comparison of multiple experiments
Side-by-side comparison of multiple experiments
No parameter schema in public metadata yet.
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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
Details
- Repository
- github.com/rohithraju-ops/mcp-ml-lab
- Package
mcp-ml-lab- Transport
- STDIO
- Registry
- MCP registry, active