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ClawGrab MCP Server

ClawGrab MCP Server

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by getclawgrab
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

get_transcript

Extract spoken-word transcripts from public video and audio URLs, with optional timestamps and summaries. Returns transcript text or a clear error instead of guessing.

Instructions

Extract the transcript (spoken words) from a public video/audio URL: YouTube, TikTok, Vimeo, Dailymotion, SoundCloud, TED, Twitch and more. Instagram, X/Twitter and Facebook usually need a login and often fail. Returns the transcript text, or an honest error - it never guesses.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "get_transcript"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesThe video or media URL to transcribe
summaryNoAlso return an AI-generated summary
timestampsNoInclude timestamps in the transcript

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the full burden. It does disclose two useful behaviors: auth-gated platforms will fail, and the tool 'never guesses' — returning an honest error instead of fabricated text. It does not cover rate limits, media length limits, or what the failure response actually looks like.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with the verb and resource, then platform scope, then failure behavior. No filler and no repetition of the title or schema fields.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-required-param extraction tool with no output schema, the description covers purpose, platform coverage, and failure semantics well. The remaining gap is the two optional flags, which the schema documents but the description never acknowledges.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so url, summary and timestamps are already documented in the schema; baseline 3 applies. The description adds no meaning on top — notably it never mentions the summary or timestamps options or how they affect the returned content.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Extract the transcript (spoken words) from a public video/audio URL') and enumerates supported platforms (YouTube, TikTok, Vimeo, SoundCloud, TED, Twitch). It never names or contrasts a sibling like translate_transcript or extract_quotes, so it is clear but lacks explicit sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete context on when the tool works versus when it fails: Instagram, X/Twitter and Facebook 'usually need a login and often fail.' This is a real eligibility boundary an agent can act on. It stops short of saying how to choose between this and text-based siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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