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)
Transcribe public audio and video URLs to clean text and SRT, with job tracking.
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.
You 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": {
"fieldscribe": {
"command": "npx",
"args": [
"-y",
"@reneturcios/fieldscribe",
"mcp"
]
}
}
}Run this in your terminal
claude mcp add fieldscribe -- npx -y @reneturcios/fieldscribe mcpPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"fieldscribe": {
"command": "npx",
"args": [
"-y",
"@reneturcios/fieldscribe",
"mcp"
]
}
}
}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": {
"fieldscribe": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@reneturcios/fieldscribe",
"mcp"
]
}
}
}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.
These 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 6, 2026
The registry name was verified with the GitHub account CloudCorpRecords, 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 (0.1.0) reached the registry yesterday.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js, Docker 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:★ 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.
fieldscribe is a small local bridge to FieldTranscript's hosted MCP server, a transcription service that turns public audio and video files into text. You hand it a URL — a YouTube video, a podcast episode, a lecture recording — and it runs the transcription as a job in the background: submitting returns a job id immediately, the job takes roughly one to ten minutes, and polling the job eventually yields the finished transcript as clean text or SRT, depending on what the upstream returns.
The bridge keeps no transcripts and no job state of its own. It serves the upstream server's tool list locally, exactly as captured from the live endpoint, and forwards each tool call upstream, returning the result untouched. One tool in the captured surface deletes — delete_job permanently removes a finished job and its stored text — and one creates work: submit_transcription starts a new transcription job on the caller's account. Everything else reads.
submit_transcription — submit a public video or audio URL (YouTube etc.) for verbatim transcription. Returns a job_id immediately. Required: url. Transcription takes about 1-10 minutes; poll get_job every 30-60 seconds until the status is done. Not for private or internal URLs.get_job — check one transcription job: its status (queued, working, done or error) and metadata. Required: job_id. Optional: include_transcript (default false) — set it only once the status is done and the full text is actually needed, because transcripts of long videos are large.list_jobs — list all transcription jobs on your account, newest first. No arguments.delete_job — permanently delete a finished (done or errored) transcription job and its stored text. Required: job_id. Refused while the job is queued or running, and it cannot be undone.FIELDTRANSCRIPT_MCP_URL (optional) — override the upstream endpoint. Defaults to https://mcp.fieldtranscript.com/mcp. (The override hook is this rebuild's own labelled reconstruction.)FIELDTRANSCRIPT_API_KEY (optional) — when set, every upstream request carries an Authorization: Bearer header. When it is not set, no auth header is sent. (This forwarding is also a labelled reconstruction; the original surface was captured keyless.)On the first tool call the client opens a streamable HTTP session with the upstream: a JSON-RPC initialize request (protocolVersion 2025-03-26, clientInfo name fieldscribe), then a notifications/initialized notification, then the tools/call request carrying the caller's arguments unchanged. When the upstream answers initialize with an Mcp-Session-Id header, that value rides on every later request of the session, which is reused for the life of the process. Replies may arrive as plain JSON or as server-sent events; both are read.
Successful upstream results pass through verbatim, including any isError flag the upstream sets. Failures — a JSON-RPC error, a non-2xx status, an unreadable response, or a request that cannot be completed — come back as an isError result whose text starts with fieldscribe error: (this rendering is this rebuild's own labelled reconstruction).
Each tool is also a subcommand taking one JSON argument string (omit it for tools that take none):
npx @reneturcios/fieldscribe submit_transcription '{"url":"https://www.youtube.com/watch?v=abc123"}'
npx @reneturcios/fieldscribe get_job '{"job_id":"job-7"}' npx @reneturcios/fieldscribe get_job '{"job_id":"job-7","include_transcript":true}' npx @reneturcios/fieldscribe list_jobs npx @reneturcios/fieldscribe delete_job '{"job_id":"job-7"}' npx @reneturcios/fieldscribe --version
Exit codes: 0 on success, 1 on an isError result, 2 on a usage error.
npx -y @reneturcios/fieldscribe mcp
MCP client config:
{
"mcpServers": { "fieldscribe": { "command": "npx", "args": ["-y", "@reneturcios/fieldscribe", "mcp"] } } }
docker run -i ghcr.io/cloudcorprecords/fieldscribe
docker run --rm ghcr.io/cloudcorprecords/fieldscribe --version
The container's default command is mcp, so the first form is a stdio MCP server; any other arguments reach the CLI normally.
npm install
npm run build npm test npm run dev
The test suite runs entirely against a stubbed JSON-RPC upstream — no live network calls.
MIT — see LICENSE.