Skore

Community MCP server for Skore — not affiliated with Probabl. It exposes MCP 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, metrics, inspection, data.analyze). Call skore_api_catalog for the exact allowlisted method names per accessor.

Repository: 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):

python3.12 -m venv .venv
source .venv/bin/activate
pip install skore-mcp

From GitHub (any time, no PyPI needed):

pip install "skore-mcp @ git+https://github.com/fanfcorp/skore-mcp.git"

From a local clone (editable for development):

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):

{
  "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:

{
  "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)
skore_hub_login	Hub auth for the current process (optional api_key)
skore_project_summarize	CSV of Project.summarize()
skore_report_metrics	Shortcut: metrics summary CSV for one report id
skore_evaluate_csv	skore.evaluate on a CSV; returns session_report_key; optional put into a project
skore_project_delete	Delete a local/hub project
skore_show_versions	skore.show_versions() output
skore_configuration_get / skore_configuration_set	Read/write skore.configuration
skore_train_test_split_csv	skore.train_test_split on a CSV → train/test CSV blobs
skore_estimator_report_from_split_csv	Build EstimatorReport from CSV; returns session_report_key
skore_compare_persisted_reports	skore.compare on reports from a Project
skore_compare_session_reports	skore.compare on in-memory session reports
skore_report_call	Call get_predictions, cache_predictions, metrics.*, inspection.*, data.*, etc.
skore_session_release	Drop a session_report_key from memory

Session keys

skore_evaluate_csv always registers the report in-memory and returns session_report_key. Use that with skore_report_call (and skore_compare_session_reports) without persisting to a project. Persisted reports use project_name + report_id (id from summarize) instead.

API coverage

Skore’s Python API is large (many classes and plot objects). This server maps one tool (skore_report_call) to the methods Skore exposes on reports and accessors, with an explicit allowlist so behavior stays predictable. Plots are returned as base64 PNG and tables as CSV in JSON when serialization supports it. It is not a line-for-line duplicate of every overload in the docs, but it covers the public patterns for reports, metrics, inspection, and data analysis.

Hub projects use project_name like workspace_slug/project_slug per Skore docs. Call skore_hub_login (or your hub plugin’s env vars) before hub operations.

Notes

• On SQLite 3.41+, the server applies a small compatibility patch for diskcache (used by local Skore storage) so local projects work on current macOS/Python builds.
• Logs go to stderr; do not print to stdout when using stdio MCP.

License

This project is licensed under the MIT License — see LICENSE. Skore and other dependencies remain under their own licenses.

Publish or clone from GitHub

PyPI via Trusted Publishing (recommended)

The repo includes .github/workflows/publish.yml. On PyPI → skore-mcp → Publishing (or when creating the project), add a pending publisher:

Field	Value
Owner	fanfcorp (or your GitHub org/user)
Repository	skore-mcp
Workflow name	publish.yml
Environment	(leave empty unless you add environment: pypi to the job)

Then merge the workflow on main, bump the version in pyproject.toml / skore_mcp/__init__.py, and create a GitHub Release (or run the workflow manually with Actions → Publish to PyPI → Run workflow). The release event triggers the upload; no long-lived PYPI_API_TOKEN is required.

Clone

After cloning:

git clone https://github.com/fanfcorp/skore-mcp.git
cd skore-mcp
python3.12 -m venv .venv && source .venv/bin/activate
pip install -e .

To push updates (replace the remote URL if your fork or username differs):

git remote add origin https://github.com/fanfcorp/skore-mcp.git
git branch -M main
git push -u origin main

Or create the repo and push in one step (with GitHub CLI authenticated):

gh repo create fanfcorp/skore-mcp --public --source=. --remote=origin --push