CourtVision Rules MCP

CourtVision Rules MCP

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

Category
访问服务器

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.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选