Model Context Protocol server for Skore (experiment tracking and model evaluation)
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# Skore MCP server Community MCP server for [Skore](https://docs.skore.probabl.ai/stable/) — not affiliated with Probabl. It exposes [MCP](https://modelcontextprotocol.io/) tools for projects, `evaluate` / `compare` / `train_test_split`, global `configuration`, and a **`skore_report_call` dispatcher** that covers the report surface documented in the Skore API (e.g. [`ComparisonReport.get_predictions`](https://docs.skore.probabl.ai/stable/reference/api/skore.ComparisonReport.get_predictions.html), metrics, inspection, `data.analyze`). Call **`skore_api_catalog`** for the exact allowlisted method names per accessor. **Repository:** [github.com/fanfcorp/skore-mcp](https://github.com/fanfcorp/skore-mcp) ## Requirements - Python **3.10+** (same as Skore 0.15) - Dependencies: `skore`, `mcp[cli]`, `pandas` (see `pyproject.toml`) ## Install **From PyPI** (after the package is published): ```bash python3.12 -m venv .venv source .venv/bin/activate pip install skore-mcp ``` **From GitHub** (any time, no PyPI needed): ```bash pip install "skore-mcp @ git+https://github.com/fanfcorp/skore-mcp.git" ``` **From a local clone** (editable for development): ```bash cd "/path/to/skore-mcp" python3.12 -m venv .venv source .venv/bin/activate pip install -e . ``` After install, run the server as **`skore-mcp`** or **`python -m skore_mcp`** (stdio MCP). ## Cursor / Claude Desktop Add a stdio server (use your real paths): ```json { "mcpServers": { "skore": { "command": "/path/to/MCP Skore/.venv/bin/python", "args": ["-m", "skore_mcp"], "env": { "SKORE_WORKSPACE": "/path/to/your/skore/workspace" } } } } ``` Alternatively, after `pip install` into an environment: ```json { "mcpServers": { "skore": { "command": "skore-mcp" } } } ``` ## Tools | Tool | Purpose | |------|--------| | `skore_info` | Skore version and mode hints | | `skore_api_catalog` | JSON allowlists for `skore_report_call` (accessors + method names…
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