CourtVision Rules MCP

CourtVision Rules MCP

Turns official pickleball scoring rules into callable tools for LLMs to track live matches accurately.

Category
Visit Server

README

CourtVision Rules MCP

CI License: MIT Python

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.

CourtVision scoring across four modes

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:

Tool What it does
pickleball_new_match Start a doubles match; returns a match_id and the opening 0-0-2 call
pickleball_record_rally Apply a rally result (server_won / server_lost) with full side-out logic
pickleball_record_fault Record a fault by either team and apply the right transition
pickleball_get_score Return the current state and three-number score call
pickleball_undo Revert the last rally or fault
pickleball_explain_rule Plain-language summary of common rules topics

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:

match_format scoring Call Rule
doubles sideout 7 4 2 Traditional two-server side-out with the one-server opening (the default).
singles sideout 7 4 One server per side; every lost rally is an immediate side-out. Serve court follows score parity.
doubles rally 7 4 A point on every rally; lose your serve and the other side scores and takes serve.
singles rally 7 4 Same rally logic, one server per side.

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.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured