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)
Local MCP server: recordings or public video URLs into transcript, frames, OCR, wall-clock evidence.
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。
TALKTHROUGH_WHISPER_MODEL, TALKTHROUGH_OCR, TALKTHROUGH_OCR_LANG, TALKTHROUGH_HOME, TALKTHROUGH_MAX_DOWNLOAD_BYTES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": {
"talkthrough": {
"command": "uvx",
"args": [
"talkthrough-mcp"
],
"env": {
"TALKTHROUGH_WHISPER_MODEL": "your-value",
"TALKTHROUGH_OCR": "your-value",
"TALKTHROUGH_OCR_LANG": "your-value",
"TALKTHROUGH_HOME": "your-value",
"TALKTHROUGH_MAX_DOWNLOAD_BYTES": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add talkthrough --env TALKTHROUGH_WHISPER_MODEL=your-value --env TALKTHROUGH_OCR=your-value --env TALKTHROUGH_OCR_LANG=your-value --env TALKTHROUGH_HOME=your-value --env TALKTHROUGH_MAX_DOWNLOAD_BYTES=your-value -- uvx talkthrough-mcp运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"talkthrough": {
"command": "uvx",
"args": [
"talkthrough-mcp"
],
"env": {
"TALKTHROUGH_WHISPER_MODEL": "your-value",
"TALKTHROUGH_OCR": "your-value",
"TALKTHROUGH_OCR_LANG": "your-value",
"TALKTHROUGH_HOME": "your-value",
"TALKTHROUGH_MAX_DOWNLOAD_BYTES": "your-value"
}
}
}
}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": {
"talkthrough": {
"type": "stdio",
"command": "uvx",
"args": [
"talkthrough-mcp"
],
"env": {
"TALKTHROUGH_WHISPER_MODEL": "your-value",
"TALKTHROUGH_OCR": "your-value",
"TALKTHROUGH_OCR_LANG": "your-value",
"TALKTHROUGH_HOME": "your-value",
"TALKTHROUGH_MAX_DOWNLOAD_BYTES": "your-value"
}
}
}
}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。
TALKTHROUGH_WHISPER_MODEL envTALKTHROUGH_OCR envTALKTHROUGH_OCR_LANG envTALKTHROUGH_HOME envTALKTHROUGH_MAX_DOWNLOAD_BYTES env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8日
注册名称已通过 GitHub 账户 korovin-aa97 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(0.4.4)于16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 Python (uv)。
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:↓ 每周下载量i★ 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。
Quickstart · Tools · Benchmarks · FAQ · Troubleshooting · Changelog · Contributing
Give Claude Code or Codex a narrated .mov/.mp4 — or a public video link — and talkthrough turns it into searchable transcript, exact frames, OCR and wall-clock timestamps, locally — so your agent writes an evidence-backed issue draft or investigates the fix.
Also works for meetings, workshops, product demos, and production incidents.
*Illustration — an animation of the /talkthrough:bug storyline, not a screen capture: the recording is indexed locally (transcript · keyframes · OCR · wall-clock), the evidence checkpoint is assembled, and the draft is ready to file with your own tracker tooling (gh, Jira, a GitHub MCP server — talkthrough itself never leaves your machine and never posts anything).*
Real runs, no animation:
bug-report.md comes out..mp4 and the unedited agent output. Every number is reproducible — processing that file gives job_id 8703a66bbe77a7d0 (the job id *is* the sha256 prefix, so shasum -a 256 predicts it), 17 keyframes / 4 unique, and 0 transcript segments because there is no audio track.One command, no system dependencies: ffmpeg falls back to a bundled build, OCR is pip-only, and whisper models download themselves on first use. The only prerequisite is uv (brew install uv or curl -LsSf https://astral.sh/uv/install.sh | sh).
Cold setup has two separate stages: uvx first resolves a compatible Python and the pinned server environment, then the first process_media downloads any missing media/model assets. A plugin update can create a new environment and, without system ffmpeg, fetch the ~80 MB bundled ffmpeg again; shared Whisper/OCR/diarization caches and warm, network-free jobs remain reusable. See Troubleshooting.
Two install paths — pick one, not both (the plugin already includes the server; installing both would register it twice):
Server only — the 9 tools + 6 prompts, and nothing else on your system. Choose this for a minimal setup, or when you manage MCP servers yourself across several clients:
claude mcp add -s user talkthrough -- uvx --python ">=3.11,<3.14" "talkthrough-mcp[diarization,url]"
Full plugin — the same server, plus native slash commands (/talkthrough:bug, /talkthrough:triage-recording, …) that handle the ceremony for you, a ready-made triage subagent, and an agent skill that teaches Claude the workflow. Choose this for the best out-of-the-box experience:
/plugin marketplace add korovin-aa97/talkthrough-mcp
/plugin install talkthrough@talkthrough
Claude Desktop
claude_desktop_config.json:
{
"mcpServers": { "talkthrough": { "command": "uvx", "args": [ "--python", ">=3.11,<3.14", "talkthrough-mcp[diarization,url]" ] } } }
More: integrations/claude-desktop/
Cursor
~/.cursor/mcp.json (or project .cursor/mcp.json):
{
"mcpServers": { "talkthrough": { "command": "uvx", "args": [ "--python", ">=3.11,<3.14", "talkthrough-mcp[diarization,url]" ] } } }
More: integrations/cursor/
OpenAI Codex CLI
~/.codex/config.toml (or project-scoped .codex/config.toml in trusted projects):
[mcp_servers.talkthrough]
command = "uvx" args = ["--python", ">=3.11,<3.14", "talkthrough-mcp[diarization,url]"]
More: integrations/codex/
Gemini CLI
~/.gemini/settings.json:
{
"mcpServers": { "talkthrough": { "command": "uvx", "args": [ "--python", ">=3.11,<3.14", "talkthrough-mcp[diarization,url]" ] } } }
More: integrations/gemini-cli/
Cline / Roo Code
cline_mcp_settings.json (via MCP Servers UI):
{
"mcpServers": { "talkthrough": { "command": "uvx", "args": [ "--python", ">=3.11,<3.14", "talkthrough-mcp[diarization,url]" ] } } }
More: integrations/cline/
OpenClaw
~/.openclaw/openclaw.json:
{
"mcp": { "servers": { "talkthrough": { "command": "uvx", "args": [ "--python", ">=3.11,<3.14", "talkthrough-mcp[diarization,url]" ] } } } }
More: integrations/openclaw/
OpenCode
opencode.json (project) or ~/.config/opencode/opencode.json:
{
"mcp": { "talkthrough": { "type": "local", "command": [ "uvx", "--python", ">=3.11,<3.14", "talkthrough-mcp[diarization,url]" ], "enabled": true } } }
More: integrations/opencode/
Goose
~/.config/goose/config.yaml:
extensions:
talkthrough: enabled: true type: stdio cmd: uvx args: ["--python", ">=3.11,<3.14", "talkthrough-mcp[diarization,url]"]
More: integrations/goose/
GitHub Copilot CLI
~/.copilot/mcp-config.json:
{
"mcpServers": { "talkthrough": { "command": "uvx", "args": [ "--python", ">=3.11,<3.14", "talkthrough-mcp[diarization,url]" ] } } }
More: integrations/copilot-cli/
Windsurf
~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": { "talkthrough": { "command": "uvx", "args": [ "--python", ">=3.11,<3.14", "talkthrough-mcp[diarization,url]" ] } } }
More: integrations/windsurf/
Zed
settings.json (Zed):
{
"context_servers": { "talkthrough": { "source": "custom", "command": { "path": "uvx", "args": [ "--python", ">=3.11,<3.14", "talkthrough-mcp[diarization,url]" ] } } } }
More: integrations/zed/
Any other MCP stdio client uses the same server command: uvx --python ">=3.11,<3.14" "talkthrough-mcp[diarization,url]". Per-engine folders with exactly these snippets plus verification steps live in integrations/; agents can self-install via llms-install.md.
Multi-person recordings (meetings, interviews, panels) can carry S1/S2/… speaker labels. Every install button, snippet, and the plugin above already ship the [diarization] engine, so asking your agent "who said what" just works — diarization itself still runs only when requested per call (process_media(path=..., diarize=true, num_speakers= )), and its models download once on first use.
Prefer the minimal server without the diarization engine? Use uvx --python ">=3.11,<3.14" talkthrough-mcp as the command instead (the MCP registry entry also resolves to this lean form) — an explicit diarize=true will then answer with the one-line install fix. Details in Speakers.
Update your plugin or generated config, then warm its exact pinned launcher before reconnecting the MCP client:
uvx --python ">=3.11,<3.14" "talkthrough-mcp[diarization,url]==0.4.4" --version
The output should name 0.4.4 and both extras. This prepares dependencies; the first recording can still need model/tool downloads. A cold environment can exceed the client's startup timeout. See startup recovery. Existing jobs need no migration.
Regenerated configs and the plugin carry [diarization,url]. A config you wrote by hand for 0.3.x — uvx --python ">=3.11,<3.14" talkthrough-mcp, or a pin without the url extra — upgrades the server in place and lists the new process_url tool, but only direct https:// media links work until the extra is there: YouTube and video pages answer with the one-line install fix. Add url to your command (uvx --python ">=3.11,<3.14" "talkthrough-mcp[diarization,url]"), restart the client, and check with talkthrough-mcp --version (0.4.1+), which names the extras the environment has; the server logs the same line to stderr at every start, so your client's MCP log shows it too.
git clone https://github.com/korovin-aa97/talkthroug只显示了开头部分;其余内容请在仓库中阅读