ml-inspector-mcp

Framework-agnostic ML model analysis MCP server. Drop in any trained model and test data — Claude evaluates it, explains predictions, detects drift, and generates PDF reports via natural language.

Installation

pip install ml-inspector-mcp                                          # minimal
pip install "ml-inspector-mcp[full]"                                  # everything
pip install "ml-inspector-mcp[sklearn-onnx,explain,reports]"          # common combo

Claude Desktop setup

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (Mac):

{
  "mcpServers": {
    "ml-inspector": {
      "command": "ml-inspector",
      "env": {
        "ANTHROPIC_API_KEY": "your-key-here",
        "MLFLOW_TRACKING_URI": "http://localhost:5000"
      }
    }
  }
}

Quick start

python examples/train_demo_model.py

Then in Claude Desktop:

"Load the demo model from examples/demo_model.onnx" "Load test data from examples/demo_test.csv" "Evaluate the model and tell me how it's performing" "Explain what drove the prediction for sample 5" "Generate a PDF evaluation report"

Model compatibility

Version mismatch fix

If you get version errors loading a .pkl or .pt file, export to ONNX first:

# scikit-learn — use the convert_to_onnx tool after loading, or:
python -c "
import joblib
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
model = joblib.load('model.pkl')
onnx_model = convert_sklearn(model, initial_types=[('input', FloatTensorType([None, N_FEATURES]))])
open('model.onnx', 'wb').write(onnx_model.SerializeToString())
"

# PyTorch
torch.onnx.export(model, dummy_input, "model.onnx", opset_version=17)

# TensorFlow / Keras
python -m tf2onnx.convert --keras model.h5 --output model.onnx

All tools

License

MIT