Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
作者提供的说明(英文原文)
YMove exercise video API: search exercises, generate workouts and multi-week programs.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"exercisedb": {
"command": "npx",
"args": [
"-y",
"@reneturcios/exercisedb",
"mcp"
]
}
}
}在终端中运行这条命令
claude mcp add exercisedb -- npx -y @reneturcios/exercisedb mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"exercisedb": {
"command": "npx",
"args": [
"-y",
"@reneturcios/exercisedb",
"mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"exercisedb": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@reneturcios/exercisedb",
"mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日
注册名称已通过 GitHub 账户 CloudCorpRecords 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.1.0)于3天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js, Docker。
同一分类中有:Figma Context MCP, Claude Real Video, Figma MCP Server
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Let any LLM watch a video locally — and search everything it has ever watched.
The Figma MCP server brings Figma design context directly into your AI workflow.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
An MCP server and CLI over the YMove exercise API, which catalogues more than 1,400 exercises with video. Six tools cover the everyday questions a training app or an agent asks of that catalogue: find exercises that fit a filter, open one exercise in full, have a workout generated for a set of muscle groups, have a whole multi-week program generated, and read the two vocabularies (muscle groups and exercise types) the filters draw from, each with its exercise counts.
Whatever the API answers comes back exactly as it answered: each tool returns a single text item carrying the upstream JSON, unchanged. Nothing is reshaped, cached or stored, which also means the API's own properties carry through — exercise video URLs, for instance, are signed by the upstream service and expire after 48 hours.
The six tools:
search_exercises — filter the catalogue by free text, muscle group, exercise type, equipment and difficulty, keep only exercises that have video, and page through the results. Every argument is optional.get_exercise — one exercise by its ID or slug: video URLs, instructions and the muscles it works.generate_workout — a structured workout with sets, reps and rest times. muscleGroup is required; it takes an array of muscle groups, and a single string is accepted and treated as a one-element array. Equipment, exercise types, difficulty, exercise count, structure, rounds and warmup / cooldown blocks are all tunable.generate_program — a multi-week training program with periodization, shaped by goal, difficulty, days per week, number of weeks and available equipment.list_muscle_groups — every muscle group in the catalogue, with its exercise count.list_exercise_types — every exercise type, with counts.Arguments that the REST API documents but the tool set does not expose are deliberately left out: the search tool has no white-background / gym-video or video-tag filters and no include / exclude video-list switches.
The API is keyed. Set your YMove key before running:
export YMOVE_API_KEY="..."
Every request carries the key as the X-API-Key header. A call made without the key fails with YMOVE_API_KEY is not set before any request leaves the process. Validation also runs before any request: a missing required argument reports : missing required argument: and a wrong-typed one reports : argument has the wrong type. An upstream failure is reported as upstream error: HTTP from ymove-api, or upstream unreachable: ymove-api when the API cannot be reached at all.
Each tool is a subcommand taking one JSON argument string:
npx @reneturcios/exercisedb search_exercises '{"muscleGroup":"chest","hasVideo":true}'
npx @reneturcios/exercisedb get_exercise '{"id":"barbell-squat"}' npx @reneturcios/exercisedb generate_workout '{"muscleGroup":["chest","triceps"],"exerciseCount":8}' npx @reneturcios/exercisedb generate_program '{"goal":"strength","weeks":6,"daysPerWeek":3}' npx @reneturcios/exercisedb list_muscle_groups '{}' npx @reneturcios/exercisedb --version
Output is the upstream JSON body, unchanged. Exit codes: 0 on success, 1 on a runtime error (including a validation, API-key or upstream failure), 2 on a usage error.
npx -y @reneturcios/exercisedb mcp
MCP client config:
{
"mcpServers": { "exercisedb": { "command": "npx", "args": ["-y", "@reneturcios/exercisedb", "mcp"], "env": { "YMOVE_API_KEY": "..." } } } }
docker run -i -e YMOVE_API_KEY="..." ghcr.io/cloudcorprecords/exercisedb
docker run --rm -e YMOVE_API_KEY="..." ghcr.io/cloudcorprecords/exercisedb list_exercise_types '{}'
npm install
npm run build # tsc -> dist/ npm test # tsx --test test/*.test.ts (fake fetch only, no network) npm run dev # run the CLI from source
MIT — Copyright (c) 2026 Rene Turcios