Framework-agnostic ML model analysis MCP server — evaluate, explain, and report on any trained model via Claude
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# ml-inspector-mcp [](https://pypi.org/project/ml-inspector-mcp/) [](https://www.python.org/) [](LICENSE) 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 ```bash 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): ```json { "mcpServers": { "ml-inspector": { "command": "ml-inspector", "env": { "ANTHROPIC_API_KEY": "your-key-here", "MLFLOW_TRACKING_URI": "http://localhost:5000" } } } } ``` ## Quick start ```bash 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 | Format | Framework | Install | |--------|-----------|---------| | `.onnx` | Any | Always works — recommended | | `.pkl` / `.joblib` | scikit-learn | `pip install "ml-inspector-mcp[sklearn-onnx]"` | | `.h5` / `.keras` | TensorFlow/Keras | `pip install "ml-inspector-mcp[tensorflow]"` | | `.pt` / `.pth` | PyTorch (full model only) | `pip install "ml-inspector-mcp[pytorch]"` | ### Version mismatch fix If you get version errors loading a `.pkl` or `.pt` file, export to ONNX first: ```bash # scikit-learn — use the convert_to_onnx to…
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