Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Description from the maker (original English)
Local MCP server: recordings or public video URLs into transcript, frames, OCR, wall-clock evidence.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
TALKTHROUGH_WHISPER_MODEL, TALKTHROUGH_OCR, TALKTHROUGH_OCR_LANG, TALKTHROUGH_HOME, TALKTHROUGH_MAX_DOWNLOAD_BYTESYou need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into 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"
}
}
}
}Run this in your terminal
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-mcpFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.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"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"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"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
TALKTHROUGH_WHISPER_MODEL envTALKTHROUGH_OCR envTALKTHROUGH_OCR_LANG envTALKTHROUGH_HOME envTALKTHROUGH_MAX_DOWNLOAD_BYTES envThese settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
The registry name was verified with the GitHub account korovin-aa97, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.4.4) reached the registry 16 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
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.
Figma’s official server gives AI your design data so it can turn designs into code or describe them accurately. Connects to Figma’s cloud.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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/talkthrougOnly the beginning is shown; read the rest in the repo