English Français العربية

io.github.rohithraju-ops/mcp-ml-lab

السجل الرسمي أخرى

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

الشيفرة المصدرية التوثيق

التثبيت

أضف هذا إلى إعدادات MCP في عميلك (Claude Desktop: claude_desktop_config.json، وCursor: ‏.cursor/mcp.json، ويستخدم VS Code وغيره الصيغة نفسها «mcpServers»).

PYPI package mcp-ml-lab
Claude Code 
claude mcp add mcp-ml-lab -- uvx mcp-ml-lab==0.1.0
إعداد JSON (Claude Desktop وCursor وVS Code) 
{
    "mcpServers": {
        "mcp-ml-lab": {
            "command": "uvx",
            "args": [
                "mcp-ml-lab==0.1.0"
            ]
        }
    }
}

التوثيق

مأخوذ من ملف README للمشروع على GitHub ويُحدَّث أسبوعيًا. تفتح الروابط على GitHub.

المحتويات
  1. mcp-ml-lab
  2. Why this exists
  3. Quick start
  4. Example queries
  5. Tools
  6. How it works
  7. Roadmap
  8. Development
  9. License

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

ToolWhat it does
inspect_dataProfile a CSV — shape, dtypes, nulls, summary stats, class balance
define_taskRegister an ML task (CSV + target + classification/regression)
run_experimentTrain one or more models, optionally tuning with Optuna
get_resultsMarkdown report with metrics, hyperparameters, feature importance
compare_runsSide-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.

الأسئلة الشائعة

ما هو io.github.rohithraju-ops/mcp-ml-lab؟

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

كيف أثبّت io.github.rohithraju-ops/mcp-ml-lab؟

يعمل على جهازك. في Claude Code: claude mcp add mcp-ml-lab -- uvx mcp-ml-lab==0.1.0. وفي العملاء الآخرين، الصق إعداد JSON من هذه الصفحة في إعدادات MCP الخاصة بهم.

هل يحتاج io.github.rohithraju-ops/mcp-ml-lab إلى مفتاح API؟

لا يذكر إدراجه في السجل أي مفتاح أو سر إلزامي. قد تتطلب بعض الميزات حسابًا لدى الخدمة التي يتصل بها.

هل io.github.rohithraju-ops/mcp-ml-lab مفتوح المصدر؟

نعم. شيفرته متاحة على GitHub بترخيص MIT.

من يصون io.github.rohithraju-ops/mcp-ml-lab؟

ينشره rohithraju-ops. يدرجه AI.tn لكنه لا يصونه ولا يوصي به.

من سجل MCP الرسمي. الإدراج ليس توصية: راجع شيفرة الخادم وصلاحياته قبل منحه الوصول إلى بياناتك.