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mcp-ml-lab MCP Server

Run end-to-end ML experiments from natural language (XGBoost, LightGBM, Optuna).

Publisher claimed. No tool list reported, and Pod has not connected to this server.

Status

Pod has not dialled mcp-ml-lab yet, so everything on this page is what its publisher reported rather than what we observed. Registries describe servers; they do not connect to them. Until a check runs, treat the tool list below as a claim.

Connect

Published as mcp-ml-lab on pypi. Runs locally.

Known issues

5 problems reported by people outside the maintainer team. Issues filed by the project's own maintainers are excluded.

Most discussed

Isolate test database from user's real SQLite store

Tests currently write to ~/.mcp-ml-lab/store.db — the user's real DB.

Scope:

  • Use a pytest fixture with a tmp_path SQLite file (or in-memory)
  • Inject the test engine via a session-factory override
  • Confirm tests no longer touch the real store

Cleanest first contribution in the repo — small, well-bounded, obviously correct.

Read the thread · 2026-05-29 · open · 0 comments

Persist trained model artifacts

Reports refit the model on every call because nothing is saved.

Scope:

  • joblib-serialize the winning pipeline (preprocessor + model) per experiment
  • Store the artifact path on the Experiment row
  • get_results / compare_runs load the artifact instead of refitting
  • This is the bridge from "demo" to "production tool"

Read the thread · 2026-05-29 · open · 0 comments

Add permutation feature importance

Reports currently use tree gain importance, which is biased toward high-cardinality features.

Scope:

  • Add sklearn.inspection.permutation_importance as an option in reporting.py
  • Surface both gain and permutation in the report, labeled clearly
  • Note the speed tradeoff in the docstring (permutation is slower)

Read the thread · 2026-05-29 · open · 0 comments

Optuna MedianPruner + CV-internal early stopping

Search currently runs full trials with no pruning.

Scope:

  • Enable MedianPruner on the Optuna study
  • Add early_stopping_rounds inside CV folds using a held-out slice of each fold's train set (never the test fold — that leaks)
  • Verify wall-clock improvement on the breast-cancer smoke test

Good entry point — self-contained in search.py.

Read the thread · 2026-05-29 · open · 0 comments

Support regression tasks

v0.1.0 is classification-only. Add regression as a task_type in define_task.

Scope:

  • Accept numeric targets; validate task_type="regression" against target dtype
  • KFold (not StratifiedKFold) for CV
  • Regression metrics: RMSE, MAE, R² — optimize RMSE in the Optuna objective
  • XGBRegressor / LGBMRegressor variants in the existing trainers

This is the highest-leverage v0.2.0 item — it roughly doubles the addressable use case.

Read the thread · 2026-05-29 · open · 0 comments

Firsthand observations

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For agents

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  • 5 reported issues below
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