CourtVision Rules MCP

A Model Context Protocol server that turns the official pickleball scoring rules into callable tools, so an LLM tracks a live match without ever improvising the rules.

Built as part of CourtVision AI — an AI pickleball companion — this is the protocol-layer piece: the small, provably correct service the model leans on so the conversation stays accurate.

Preview

The same seven rallies, scored four ways — doubles/singles × side-out/rally. One engine, all correct, all unit-tested.

Related MCP server: mcp-odds-api

Why it exists

Pickleball's side-out scoring is exactly the kind of thing language models get almost right. The one-server rule at the start, the server-1-to-server-2 rotation, win-by-two — a model that holds these in its head drifts after a few turns. The fix is not a better prompt. It is moving the part that must be exact out of the model and into a tool.

That is the entire MCP thesis in one server, and it doubles as a design rule this product was built on: accuracy is the brand. A wrong call in front of four players ends the app's life with that crew. So the engine never guesses — ambiguous input is rejected with an actionable message rather than resolved silently.

What it does

Six tools, exposed over MCP:

State is held server-side, keyed by match_id, so a client runs a whole match across many turns without re-sending the board.

Architecture

The rules logic and the transport are deliberately separate:

pickleball_engine.py   pure rules engine, zero dependencies, unit-tested
                       (doubles + singles, side-out + rally scoring)
server.py              thin MCP wrapper: input validation + state registry
tests/                 the rules engine's safety net (26 cases)
evaluation/            MCP eval questions an agent must answer using the tools

The engine is the load-bearing part, so it lives on its own where it can be tested in isolation and reused by any surface — the MCP server today, the Match Mode web app and native clients later.

Run it

python -m venv .venv && source .venv/bin/activate
pip install "mcp[cli]"

# run the server (stdio transport)
python server.py

# or inspect it interactively
npx @modelcontextprotocol/inspector python server.py

Use it from Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "courtvision-rules": {
      "command": "python",
      "args": ["/absolute/path/to/courtvision-rules-mcp/server.py"]
    }
  }
}

Then ask Claude: "Start a pickleball match between the Reds and the Blues. The Reds win the first rally, then lose the next one — what's the score?" It will call the tools and answer 0 1 1, correctly, every time.

Tests

pip install pytest
python -m pytest tests/ -q

The tricky cases are pinned: the one-server match start, the server-1 → server-2 → side-out rotation, win-by-two, and that only the serving team can score.

Notes

Rules summaries defer to the current USA Pickleball Official Rulebook — this server computes scoring and explains the common cases; it does not reproduce the rulebook.

Formats

Set match_format and scoring when you start a match:

Doubles side-out remains the default, so existing callers are unaffected. Rally games are commonly played to 15 or 21, win by 2 — pass target/win_by to set them.

License

MIT — see LICENSE.

Run as a service (Docker)

docker build -t courtvision-rules-mcp .
docker run -i --rm courtvision-rules-mcp        # stdio transport

The server speaks MCP over stdio by default (what Claude Desktop and local MCP clients use). For a hosted deployment, set MCP_TRANSPORT=sse to serve over HTTP/SSE. Console script after pip install -e .: courtvision-rules-mcp.