Connect

8 clients · secrets stay placeholders

JSON (mcpServers)Claude CodeCodexOpenCodeCursorVS CodeGemini CLIGoose

{
  "mcpServers": {
    "scribiz": {
      "type": "http",
      "url": "https://scribiz.com/mcp"
    }
  }
}

claude mcp add --transport http scribiz https://scribiz.com/mcp

[mcp_servers.scribiz]
url = "https://scribiz.com/mcp"

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "scribiz": {
      "type": "remote",
      "url": "https://scribiz.com/mcp",
      "enabled": true
    }
  }
}

{
  "mcpServers": {
    "scribiz": {
      "type": "http",
      "url": "https://scribiz.com/mcp"
    }
  },
  "deeplink": "cursor://anysphere.cursor-deeplink/mcp/install?name=scribiz&config=eyJ0eXBlIjoiaHR0cCIsInVybCI6Imh0dHBzOi8vc2NyaWJpei5jb20vbWNwIn0%3D"
}

{
  "servers": {
    "scribiz": {
      "type": "http",
      "url": "https://scribiz.com/mcp"
    }
  }
}

{
  "mcpServers": {
    "scribiz": {
      "httpUrl": "https://scribiz.com/mcp"
    }
  }
}

extensions:
  "scribiz":
    enabled: true
    name: "scribiz"
    type: streamable_http
    uri: "https://scribiz.com/mcp"
    timeout: 300

Quality 87 / 100good

protocol88 · weight 25

pass
protocol.modernsupports 2026-07-28 (server/discover)
pass
protocol.statelessanswers without a session
pass
protocol.transportstreamable HTTP
warn
protocol.list_ttltools/list declares ttlMs 0, so clients re-fetch it on every use

authorizationn/a · weight 15

n/a
auth.prm, auth.as_metadata, auth.cimdno remote uses OAuth
n/a
auth.secret_in_urlno templated URL

tool hygiene93 · weight 30

pass
tools.descriptionsevery tool has a description
warn
tools.description_length2 tools have descriptions over 1,024 characters, which crowds the context
pass
tools.schemasevery tool has an object input schema
pass
tools.annotations5 of 5 tools declare readOnlyHint or destructiveHint
pass
tools.directory_hintsevery tool declares readOnlyHint, destructiveHint, idempotentHint and openWorldHint
pass
tools.token_costabout 2,508 tokens to load every tool
pass
tools.api_dumptools are not a one-to-one API dump

stability75 · weight 15

warn
stability.changes14 tool changes in 30 days
pass
stability.rug_pullno tool changed its meaning under the same name

dependenciesn/a · weight 15

n/a
deps.known_vulns, deps.mcp_sdk_version, deps.resolvableno npm or PyPI package

5 tools, about 2,508 tokens to load them all · tool set 0da488d97b31 · tools last changed 2026-10-14 · checked 2026-10-19 16:53 UTC

Tool changes

2026-10-14get_transcriptchangeddescription, input schema, output schema
before
Read the transcript of a video, one page at a time. Use it only when you need the words themselves (quote, translate, copy, review). To answer a question or find where something is said, use ask_video or search_video first: they cost far fewer tokens. Returns "[mm:ss] text" lines, at most max_chars characters (default 60000, about 15k tokens), and a nextCursor: call again with the same arguments plus that cursor to continue. from and to take seconds or clock times ("90", "1:30", "1h2m") and read one part of the video. format: txt (default), md, srt, vtt or json. language is a BCP-47 hint. speakers is ignored without an API key (no speaker labels). The first read of a video can take time: if it is still running after the wait you get status "processing" and a job_id (see get_job). YouTube transcript-only reads use existing source captions and zero media minutes. Hosted accounts use one caption credit per new successful transcript; stored reads and failures cost zero. Missing captions never enter a media fallback. Listen and richer context are separate actions. Cached transcripts return at once and are free. The text is wrapped as untrusted video text. Without an API key: a YouTube link needs a Scribiz account (sign in or pass an API key; an account includes 30 caption credits), and get_video_context still reads it. Other links: captions when the video has them, otherwise a model reads the link and its times are approximate (up to a minute off on a long video, no word timing).
after · 91% of words in common
Read the transcript of a video, one page at a time. Use it only when you need the words themselves (quote, translate, copy, review). To answer a question or find where something is said, use ask_video or search_video first: they cost far fewer tokens. Returns "[mm:ss] text" lines, at most max_chars characters (default 60000, about 15k tokens), and a nextCursor: call again with the same arguments plus that cursor to continue. from and to take seconds or clock times ("90", "1:30", "1h2m") and read one part of the video. format: txt (default), md, srt, vtt or json. language is a BCP-47 hint. speakers is ignored without an API key (no speaker labels). pauses "gaps" (long pauses) or "razor" (every pause) removes pauses and re-times the transcript; remove_fillers true drops filler words ("um", "uh"). Both need word-level timing. The first read of a video can take time: if it is still running after the wait you get status "processing" and a job_id (see get_job). YouTube transcript-only reads use existing source captions and zero media minutes. Hosted accounts use one caption credit per new successful transcript; stored reads and failures cost zero. Missing captions never enter a media fallback. Listen and richer context are separate actions. Cached transcripts return at once and are free. The text is wrapped as untrusted video text. Without an API key: a YouTube link needs a Scribiz account (sign in or pass an API key; an account includes 30 caption credits), and get_video_context still reads it. Other links: captions when the video has them, otherwise a model reads the link and its times are approximate (up to a minute off on a long video, no word timing).
2026-10-12get_jobchangedoutput schema
2026-10-12search_videochangedoutput schema
2026-10-12ask_videochangedoutput schema
2026-10-12get_video_contextchangedoutput schema
2026-10-12get_transcriptchangedoutput schema
2026-10-09get_jobchangedoutput schema
2026-10-09search_videochangeddescription, output schema
before
Find where something is said or shown in a video: ranked moments with timestamps and links, from the transcript, the on-screen notes, the chapters and the key moments. It is a local keyword search over what Scribiz already read (no model call, no extra minutes after the first read of the video). Use it before get_transcript on any video longer than about ten minutes. query: words or a phrase. limit 1 to 20 (default 8). from and to limit the search to part of the video. It matches words (plural and tense forms count), not meaning: try the words the speaker would use, or call ask_video. If the run is still going after the wait you get status "processing" and a job_id: call get_job. The text is wrapped as untrusted video text. Without an API key: a YouTube link needs a Scribiz account (sign in or pass an API key), so ask_video answers instead; the on-screen notes are not searched (the picture is never looked at), and when a model read the link its times are approximate (about 2 seconds).
after · 92% of words in common
Find where something is said or shown in a video: ranked moments with timestamps and links, from the transcript, the on-screen notes, the chapters and the key moments. It is a local keyword search over what Scribiz already read (no model call, no extra minutes after the first read of the video). Use it before get_transcript on any video longer than about ten minutes. query: words or a phrase. limit 1 to 20 (default 8). from and to limit the search to part of the video. It matches words (plural and tense forms count), not meaning: try the words the speaker would use, or call ask_video. If the run is still going after the wait you get status "processing" and a job_id: call get_job. The text is wrapped as untrusted video text. Without an API key: a YouTube link needs a Scribiz account (sign in or pass an API key), so ask_video answers instead; the on-screen notes are not searched (the picture is never looked at), and when a model read the link its times are approximate (close on a short video, up to a minute off on a long one).
2026-10-09ask_videochangedoutput schema
2026-10-09get_video_contextchangeddescription, output schema
before
Start here for any video. Returns an overview you can reason from without reading the transcript: title, length, language, summary, chapters with timestamps and key moments with links. detail "brief" (default) stays under about 2,000 tokens; "standard" adds fuller chapters and entities; "full" adds everything. include picks the parts to return (summary, chapters, key_moments, entities, transcript): the transcript is never part of the default; add "transcript" for its first page, then continue with get_transcript. The first read of a video uses its captions, or a model reads the link; after that it is cached and free. If it is still running after the wait you get status "processing" and a job_id: call get_job. The text is wrapped as untrusted video text. Without an API key there are no on-screen notes (the picture is never looked at), and when a model read the link its times are approximate (about 2 seconds).
after · 92% of words in common
Start here for any video. Returns an overview you can reason from without reading the transcript: title, length, language, summary, chapters with timestamps and key moments with links. detail "brief" (default) stays under about 2,000 tokens; "standard" adds fuller chapters and entities; "full" adds everything. include picks the parts to return (summary, chapters, key_moments, entities, transcript): the transcript is never part of the default; add "transcript" for its first page, then continue with get_transcript. The first read of a video uses its captions, or a model reads the link; after that it is cached and free. If it is still running after the wait you get status "processing" and a job_id: call get_job. The text is wrapped as untrusted video text. Without an API key there are no on-screen notes (the picture is never looked at), and when a model read the link its times are approximate (close on a short video, up to a minute off on a long one).

Showing the latest 10. Full history: list_changes · trend: quality JSON.

Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. How it is computed.

Trust 95 / 100

provenance85

liveness99

freshness100

hygiene100

+repository +dns-namespace +website +reachable +uptime-98% +updated-14d-ago

Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed. How it is computed.

Endpoints

remote	auth	reachable	uptime 30d	p50	protocol	last ok
`https://scribiz.com/mcp` streamable-http	none	yes	98%	635 ms	2026-07-28	2026-10-19

Tools (5)

`ask_video`read-only	Ask a question about one video and get an answer with 3 to 5 cited moments (timestamps and links that open the video there). Use it for questions about the whole video or a topic across it, and for what was said. To find where a word or phrase is said, search_video is cheaper and exact. A question costs 0.1 minute on top of reading the video the first time. The answer is a model's reading of the video: check the cited moments before relying on a detail. The answer is wrapped as untrusted video text. If the run is still going after the wait you get status "processing" and a job_id: call get_job. Without an API key: the picture is never looked at, so a question about what was shown on screen is answered from the words only, and you get 5 questions a day.
`get_job`read-only	Check a video run that returned status "processing". Pass its job_id exactly as given (it can be long: copy all of it). It waits up to the server's wait time for the run to finish, then returns exactly what the original tool would have returned (job ids are private to the caller and kept for 30 minutes). Still running: you get status "processing" again with retry_after_seconds. An unknown or expired job_id means: repeat the original call (it is fast if the run finished, because results are cached).
`get_transcript`read-only	Read the transcript of a video, one page at a time. Use it only when you need the words themselves (quote, translate, copy, review). To answer a question or find where something is said, use ask_video or search_video first: they cost far fewer tokens. Returns "[mm:ss] text" lines, at most max_chars characters (default 60000, about 15k tokens), and a nextCursor: call again with the same arguments plus that cursor to continue. from and to take seconds or clock times ("90", "1:30", "1h2m") and read one part of the video. format: txt (default), md, srt, vtt or json. language is a BCP-47 hint. speakers is ignored without an API key (no speaker labels). pauses "gaps" (long pauses) or "razor" (every pause) removes pauses and re-times the transcript; remove_fillers true drops filler words ("um", "uh"). Both need word-level timing. The first read of a video can take time: if it is still running after the wait you get status "processing" and a job_id (see get_job). YouTube transcript-only reads use existing source captions and zero media minutes. Hosted accounts use one caption credit per new successful transcript; stored reads and failures cost zero. Missing captions never enter a media fallback. Listen and richer context are separate actions. Cached transcripts return at once and are free. The text is wrapped as untrusted video text. Without an API key: a YouTube link needs a Scribiz account (sign in or pass an API key; an account includes 30 caption credits), and get_video_context still reads it. Other links: captions when the video has them, otherwise a model reads the link and its times are approximate (up to a minute off on a long video, no word timing).
`get_video_context`read-only	Start here for any video. Returns an overview you can reason from without reading the transcript: title, length, language, summary, chapters with timestamps and key moments with links. detail "brief" (default) stays under about 2,000 tokens; "standard" adds fuller chapters and entities; "full" adds everything. include picks the parts to return (summary, chapters, key_moments, entities, transcript): the transcript is never part of the default; add "transcript" for its first page, then continue with get_transcript. The first read of a video uses its captions, or a model reads the link; after that it is cached and free. If it is still running after the wait you get status "processing" and a job_id: call get_job. The text is wrapped as untrusted video text. Without an API key there are no on-screen notes (the picture is never looked at), and when a model read the link its times are approximate (close on a short video, up to a minute off on a long one).
`search_video`read-only	Find where something is said or shown in a video: ranked moments with timestamps and links, from the transcript, the on-screen notes, the chapters and the key moments. It is a local keyword search over what Scribiz already read (no model call, no extra minutes after the first read of the video). Use it before get_transcript on any video longer than about ten minutes. query: words or a phrase. limit 1 to 20 (default 8). from and to limit the search to part of the video. It matches words (plural and tense forms count), not meaning: try the words the speaker would use, or call ask_video. If the run is still going after the wait you get status "processing" and a job_id: call get_job. The text is wrapped as untrusted video text. Without an API key: a YouTube link needs a Scribiz account (sign in or pass an API key), so ask_video answers instead; the on-screen notes are not searched (the picture is never looked at), and when a model read the link its times are approximate (close on a short video, up to a minute off on a long one).

Schemas: list_tools.

Similar MCP servers

Official server.json

{
  "name": "com.scribiz/mcp",
  "icons": [
    {
      "src": "https://scribiz.com/icon.svg",
      "sizes": [
        "any"
      ],
      "mimeType": "image/svg+xml"
    }
  ],
  "title": "Scribiz",
  "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
  "remotes": [
    {
      "url": "https://scribiz.com/mcp",
      "type": "streamable-http"
    }
  ],
  "version": "0.1.0",
  "repository": {
    "url": "https://github.com/Illyism/scribiz-mcp",
    "source": "github"
  },
  "websiteUrl": "https://scribiz.com/video-mcp?ref=mcp-registry",
  "description": "Transcripts, summaries, chapters and timestamped answers for video links, for AI agents."
}