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mohamdben-yahia

YouTube MCP Server

get_video_transcript

Get full YouTube video transcripts or subtitles in text, timestamped, or JSON format by providing a video ID or URL, with optional language and time-range filters.

Instructions

Retrieve full video transcripts or subtitles without requiring an API key.

Args: video_id_or_url: YouTube video ID or full URL (e.g., 'dQw4w9WgXcQ' or 'https://youtu.be/...'). languages: Language priority list (e.g., ['en', 'es']). Defaults to ['en']. format: Output format: 'text' (concatenated string), 'timestamped' (lines with [hh:mm:ss]), or 'json' (array of segment objects). start_seconds: Optional start timestamp to filter segments. end_seconds: Optional end timestamp to filter segments.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNotext
languagesNo
end_secondsNo
start_secondsNo
video_id_or_urlYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations present, the description carries the full behavioral burden. It discloses available output formats ('text', 'timestamped', 'json'), optional time-frame filtering, and language priority handling. It could mention failure scenarios (e.g., videos without captions), but core behavior is well covered.

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?

The description is tightly organized: a one-sentence purpose followed by a compact parameter list with inline examples and defaults. Every sentence adds functional value, and the structure enables quick parsing by an agent.

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?

All five parameters are covered with examples and defaults, and an output schema exists so return-value documentation is not necessary. The only gap is edge-case guidance such as what happens when no transcript exists or when no language matches, but this doesn't undermine usability for a straightforward retrieval tool.

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

Parameters5/5

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

Schema description coverage is 0%, yet the description independently documents every parameter with examples and semantics. It explains format variants, language priority lists, default behavior, and how start/end seconds filter segments, fully compensating for the bare schema.

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?

The description clearly states a specific action ('Retrieve full video transcripts or subtitles') and the resource (YouTube videos), with a notable no-API-key advantage. This distinguishes it from the sibling tools, which focus on analytics, search, and channel strategy rather than transcript extraction.

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?

The description makes the use case explicit: obtaining transcript or subtitle content from a YouTube video, with no API key required. It does not explicitly name alternative tools or state when not to use it, but the resource is so specific that the intended context is clear.

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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