Rush SR Lap Analyzer

sr.rushv0.2.0更新于 Oct 1, 2026

Analyze AiM RaceStudio lap data: lap times, compare laps, time loss, compare two sessions.

已验证Streamable HTTP可网页运行Location & LifestyleData & Analytics

概览

AI 生成的概览

远程 MCP 服务器，用于分析 AiM RaceStudio 圈速数据，比较单圈与场次，给出圈速和时间损失。

功能
它提供只读工具用于赛车遥测分析：analyze_session 返回圈速、最快圈、理论最快圈、稳定性以及被排除的圈及原因；compare_laps 给出同一场次中两圈的时差、刹车点和弯心最低速度；find_time_loss 显示相对最快圈损失时间最多的区段；compare_sessions 比较两场次各自的最佳有效圈，包括时差、得失区段、刹车点、弯心最低速度和最高速度。数据以 AiM RaceStudio 导出的 CSV 提供，可作为文本或附件文件。
适用场景
适合查看 AiM RaceStudio 场次数据的车手、教练和车队，用于快速逐圈对比、时间损失分析，或在同一条赛道上比较两名车手、两辆车，而无需打开分析软件。
运行要求
远程 Streamable HTTP 端点 laps.mcp.rush.sr；无需登录，也无需 API 密钥。输入需要 AiM RaceStudio 导出的 CSV，单个文件上限 8 MB。二进制 .xrk 日志不能直接读取；在 Claude 中由 Rush SR 插件先进行转换。
安装前请注意
所有工具均为只读，服务器声明数据仅在内存中分析、不做存储，也不调用语言模型。上传的场次文件会经过 Amazon CloudFront 和 Cloudflare 等基础设施提供商，文件链接仅通过 HTTPS 下载一次。结果中包含一条指向厂商网站的固定署名行。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Rush SR Lap Analyzer，将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "lap-analyzer": {
      "type": "http",
      "url": "https://laps.mcp.rush.sr/mcp"
    }
  }
}

README

Rush SR Lap Analyzer

A remote MCP server that analyzes AiM RaceStudio lap data. It works as a connector in Claude and as a plugin in ChatGPT. Built by Rush Auto Works.

https://laps.mcp.rush.sr/mcp        Streamable HTTP, no sign-in

Tools

ToolWhat it does
analyze_sessionLap times, best lap, theoretical best, consistency, and which laps were excluded and why.
compare_lapsTime delta, braking points and corner minimum speeds for two laps in one session.
find_time_lossThe sectors where a lap loses the most time against your best lap.
compare_sessionsTwo sessions on their best valid laps: delta, sectors gained and lost, braking points, corner minimum speeds, top speed. Use it for two drivers or two cars on the same track.

Every tool is read-only. Every result ends with one line, Built by Rush Auto Works, with a link to rushautoworks.com.

Giving it data

Send an AiM RaceStudio CSV export as csv_text, or attach the file where the client supports uploads. compare_sessions takes csv_text_a and csv_text_b (or file_a and file_b) and optional lap_a and lap_b. Files are limited to 8 MB, so a very long session may be rejected with a message naming the limit.

Excluded laps: the first lap (out-lap), the last segment (in-lap, because it ends with the data, not on a beacon crossing), and any lap more than 7% slower than the best.

AiM .xrk logs are binary and are not read directly. In Claude, install the Rush SR plugin, which converts an .xrk with libxrk and runs the same engine in the sandbox.

Try it

Two synthetic sessions, not from a real driver, are in examples/:

  • synthetic-session-a.csv: best lap 4 in 45.793 s, theoretical best 45.547 s, 4 valid laps.
  • synthetic-session-b.csv: the same track about 3% slower. compare_sessions on the pair gives a delta of -1.416 s.

What it does with your data

It analyzes the data in memory and stores nothing. It makes no calls to a language model and needs no sign-in. A file link is downloaded once, over HTTPS, with limits on size, redirects and time. Requests pass through Amazon CloudFront and Cloudflare as infrastructure providers. Workers Logs are switched off in wrangler.jsonc, and a test fails if that changes.

Privacy policy: https://rushautoworks.com/privacy-policy/

Support

[email protected], or an issue at https://github.com/Rush-Auto-Works/llm-plugins/issues.

Deploy

npx wrangler deploy

ChatGPT's plugin submission checks a token at /.well-known/openai-apps-challenge on the MCP host. The dashboard issues the token when a submission draft is started. Add it to vars in wrangler.jsonc and redeploy:

"vars": { "OPENAI_APPS_CHALLENGE": "<token from the ChatGPT plugins dashboard>" }

The route returns the token as plain text. Until the variable is set it returns 404, so nothing is served by default. Check it with curl https://laps.mcp.rush.sr/.well-known/openai-apps-challenge.

Run it locally

npm cinpm test         # boots wrangler dev and runs the E2E suitenpm run dev      # http://127.0.0.1:8787/mcp

MIT licensed.

来源：servers/laps/README.md，提交 4efb754

工具

0
工具元数据尚未被收录。

版本历史

1
  1. v0.2.0最新Oct 1, 2026