Any social-video URL β transcript, metadata, frames, OCR, summary, search, Q&A. MCP server + x402.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)
Any social-video URL β answers, transcript, metadata, insights, frames & on-screen text (OCR).
Agent-first video data API + MCP server. Pay per call, or with x402 (USDC) β no account.
FrameFetch turns one YouTube, YouTube Shorts, TikTok, Instagram Reels, Pinterest, or Reddit video URL into a single JSON response: a direct answer to a question about the video, metadata, engagement insights, a transcript (captions or Whisper), an LLM digest (text or spoken mp3), structured JSON (chapters/entities/products/claims), comments + sentiment, parametrically-sampled frames (every Nth / 1-per-second / a time range, at any width), and the on-screen text burned into those frames (OCR β captions, price tags, signage). Plus keyword search when you don't have a URL yet, and batch for up to 10 URLs in one call. Built API-first and MCP-first for AI agents.
This repo is the open-source client + docs. The service itself runs at framefetch.net β you bring a free API key (or pay per call with x402); the backend stays hosted.
An LLM can't watch a video. To reason about one it needs the video turned into text and images first β an answer, a transcript, metadata, a few frames. FrameFetch returns all of that from a URL, across six platforms, through one schema.
Node 18+ (uses built-in fetch). Get a free key: framefetch.net.
Version note. This repo is at 0.4.0. The newest version currently on npm is 0.3.0 β
npm install framefetchstill gives you that one, and it has onlyextract/metadata/transcript/frames/platforms/status/demo/createKey. Everything else documented below is live on the API today and available from this repo; from npm 0.3.0 you can reach the same data throughextract({ fields: [...] }).
A direct question about a video returns a short, grounded answer with timestamped quotes, instead of you parsing a 25,000-token transcript yourself.
Charged only when an answer is actually produced. A repeat question about an already-extracted video reuses the cached transcript, so it answers fast without a re-download or re-transcription β but the answer itself is always freshly generated, never cache-served.
Frames-based answers: when a video has no transcript (e.g. Pinterest, or transcription failed), the answer is grounded in sampled keyframe images instead. Then coverage.mode is "frames", quotes is [] (no transcript text to quote), and confidence is capped at "medium".
Note the two spellings: text_overlay is the request field name, textOverlay is the response key.
Every helper above is a thin wrapper over extract(), so anything extract() accepts (translate,
format, extra fields, β¦) can be passed as the last argument and is forwarded unchanged.
One failing URL never fails the batch β each entry carries its own ok flag and, when ok is false,
an error with code/message/hint. Per-URL frames specs are not accepted in a batch; use
extract() for those.
FrameFetch ships an MCP server (Streamable HTTP) with four tools: framefetch_extract,
framefetch_platform_capabilities, framefetch_search and framefetch_account. Add it to Claude,
Cursor, or any MCP client:
Or one line:
MCP lives at https://framefetch.net/mcp and speaks JSON-RPC over Streamable HTTP. REST lives under
/v1/* and takes plain JSON ({"url": "β¦"}). Crossing the two is the single most common first-call
mistake, so both directions answer clearly: a REST body POSTed to /mcp comes back as a JSON-RPC
parse error, and a JSON-RPC body POSTed to /v1/extract comes back as 400 WRONG_ENDPOINT naming
the right URL for your client.
Prefer a local stdio server (Claude Desktop, sandboxes, no inbound HTTP)? This package
ships framefetch-mcp, a zero-dependency stdioβHTTP bridge that exposes the same tools
and forwards calls to framefetch.net:
tools/list works with no key; tool calls use FRAMEFETCH_API_KEY (or x402). Override the
endpoint with FRAMEFETCH_MCP_URL.
Autonomous agents can pay per call in USDC via x402 on Base β no signup, no human in the loop. Discoverable in the x402 Bazaar and at /.well-known/x402.json. Humans can use a free tier, prepaid credits, or a Stripe card.
Failed calls throw FrameFetchError with .status, .code, and .hint:
| Method | Endpoint | Auth |
|---|---|---|
extract({ url, fields, frames, β¦ }) | POST /v1/extract | key |
ask(url, question) | POST /v1/extract (ask param) | key |
metadata(url) | POST /v1/metadata | key |
transcript(url, { translate, format }) | POST /v1/transcript | key |
frames(url, spec) | POST /v1/frames | key |
digest(url) | POST /v1/extract (digest) | key |
audioDigest(url, { voice }) | POST /v1/extract (audio_digest) | key |
structured(url) | POST /v1/extract (structured) | key |
comments(url, { comments_cap }) | POST /v1/extract (comments) | key |
commentSentiment(url) | POST /v1/extract (comment_sentiment) | key |
search(query, { limit }) | POST /v1/search | key |
batch(urls, { fields }) | POST /v1/batch | key |
platforms() | GET /v1/platforms | β |
status() | GET /v1/status | β |
demo(url) | POST /v1/demo | β |
createKey(email) | POST /v1/keys | β |
extract() request shapeSee index.d.ts for the complete typed response shape (ExtractResult, Ask,
VideoStructured, VideoComments, CommentSentiment, AudioDigest, SearchResult,
BatchResult, β¦).
Full OpenAPI: framefetch.net/openapi.json Β· Docs: framefetch.net/docs
Website Β· Docs Β· Pricing Β· Status Β· Guide: giving an agent video data Β· Compare vs alternatives
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