highbrow_fame data APIs (pay per call)

Transcript of one YouTube video

youtube_transcript

Read-only

Transcript of one YouTube video from its link or 11-character ID: full text plus timed segments, title, channel, length, language and whether the captions are auto-generated; optional language choice and translation. Suited to summarising or quoting a video. $0.01 per call.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "youtube_transcript"
  }
}

Input Schema

Name	Required	Description
`lang`	No	Caption language code, e.g. en, de, hu (default en).
`video`	No	A YouTube link or the 11-character video ID.
`format`	No	text (default), srt or vtt.
`translate`	No	Translate to the chosen language when no captions exist in it.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 10, 2026

TDQS

A3.9/5.0

Behavior3/5

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

Annotations already declare readOnlyHint and openWorldHint, so safety is covered. The description adds genuinely non-schema context — the exact returned fields and a per-call price ($0.01) — which is real value. However it omits failure behavior (private videos, disabled captions, missing translation) that would matter for an open-world fetch.

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

Conciseness4/5

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

Front-loaded with the resource and payload, followed by use case and price — no filler sentences. The first sentence is clause-dense (payload enumeration, input forms, language/translation), which slightly hurts scannability but wastes no words.

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?

With no output schema, the description correctly compensates by enumerating the returned fields, and it covers input forms, format options, and cost. It does not address edge cases such as unavailable captions or what happens when translate targets an unsupported language, so it falls just short of fully complete.

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 all four parameters (lang, video, format, translate) are already documented. The description only restates 'optional language choice and translation' and the ID form, adding little beyond the schema; the format parameter is not mentioned in the description at all. Baseline 3 applies.

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

Purpose5/5

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

States a specific verb/resource ('Transcript of one YouTube video') and instantly defines the input forms ('link or 11-character ID') plus the returned payload (full text, timed segments, title, channel, length, language, auto-generated flag). An agent knows exactly what this returns without opening the schema.

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 a concrete use-case ('Suited to summarising or quoting a video'), which tells the agent when this tool pays off. There are no exclusions or named alternatives, but the sibling set is unrelated, so the missing exclusions are low-cost.

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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