framefetch_extractLABSCO SUMMARY
Ask the video a question instead of parsing its transcript.
The answer path is what earns the install: a grounded reply with timestamped quotes, charged only when an answer is actually produced, and cached transcripts so a follow-up question is fast without re-downloading. When there is no transcript at all, the answer is grounded in sampled frames instead — and the response says so, with confidence capped and no quotes to give.
What it is
A hosted video-data API with an MCP server in front of it, plus this open-source client and docs. A model cannot watch a video; this converts one into an answer, text and images first. It covers YouTube, YouTube Shorts, TikTok, Instagram Reels, Pinterest and Reddit.
What you get
framefetch_extract— the main call: metadata, insights, transcript, frames, on-screen text, digest, spoken audio digest, structured chapters and entities, comments and comment sentimentframefetch_searchfor finding a video when you do not have a URL yet,framefetch_platform_capabilitiesfor what each platform supports,framefetch_accountfor your key's state- Question answering that returns a short grounded answer with verbatim timestamped quotes and a confidence level, instead of a transcript you have to parse
- Frames sampled the way you ask — every Nth, one per second, or a time range — at any width, with OCR over them
- Transcript translation into one of 25 languages, and subtitle export as SRT or VTT
- Batch extraction of up to 10 URLs in one call, where a single failing URL carries its own error rather than failing the batch
Requirements
A FrameFetch API key, free to create, or an agent wallet that can pay per call with x402 in USDC on Base — no account in that case. Connect remotely by pointing a client at https://framefetch.net/mcp with the key in an Authorization header, or run the bundled stdio bridge with npx -y framefetch-mcp and FRAMEFETCH_API_KEY set. Node 18+ for the client package. Two details worth knowing: this repo is at 0.4.0 while npm currently serves 0.3.0, where the extra helpers are reached through extract with a fields list; and on-screen text uses text_overlay on the request but comes back as textOverlay in the response. MIT licensed.
Setup effort
One command plus a key — claude mcp add --transport http framefetch https://framefetch.net/mcp --header "Authorization: <YOUR_FRAMEFETCH_KEY>"
USE CASES
01
Getting one fact out of a long video
Ask the question directly and read the short answer with the timestamps it came from, rather than scanning a transcript.
02
Reading text that only exists on screen
Sample frames across the video and OCR them, which is how you get captions, price tags and signage that were never spoken.
03
Researching a topic from video you have not found yet
Search for candidates, then pull metadata and digests for several of them in one batch call.
4 tools
Listed in project docs. Your client may show a different list.
framefetch_platform_capabilitiesframefetch_searchframefetch_accountBEFORE YOU INSTALL
What to know first
Tools you get
4 tools named in the project's documentation.
Requires
- A client that can reach a remote MCP server over HTTP
- Node 18+
- FRAMEFETCH_API_KEY in your environment
Where it runs
Of the 4 documented clients, 1 runs the server on your machine and 3 connect to a hosted endpoint.
INSTALLATION
Connect your client
Server setup
claude mcp add
Claude Code
TERMINAL
claude mcp add --transport http framefetch https://framefetch.net/mcp --header "Authorization: <YOUR_FRAMEFETCH_KEY>"Hosted endpoint
ENDPOINT URL
https://framefetch.net/mcpAdd this hosted endpoint to your MCP client using the credentials listed under Requirements.
MCP Clients
Cursor, Other MCP clients
JSON
{
"mcpServers": {
"framefetch": {
"url": "https://framefetch.net/mcp",
"headers": {
"Authorization": "<YOUR_FRAMEFETCH_KEY>"
}
}
}
}Claude Desktop — stdio
CLAUDE DESKTOP — STDIO · JSON
{
"mcpServers": {
"framefetch": {
"command": "npx",
"args": [
"-y",
"framefetch-mcp"
],
"env": {
"FRAMEFETCH_API_KEY": "<YOUR_FRAMEFETCH_KEY>"
}
}
}
}
