Skip to main content
Glama
AgentX-ai

YouTube DLP MCP Server

by AgentX-ai

get-video-subtitles

Extract subtitles and captions from YouTube videos by providing a URL, selecting languages, and including auto-generated captions.

Instructions

Extract subtitles and captions from a YouTube video

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesYouTube video URL to extract subtitles from
languagesNoList of language codes to extract (e.g., ['en', 'es']). If not provided, all available languages will be extracted.
include_auto_captionsNoWhether to include auto-generated captions (default: true)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, but it only states the basic action. It does not mention whether authentication is needed, what the output format looks like, whether auto-generated captions are handled specially, or any other relevant behavior beyond what the schema already conveys.

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 a single, direct sentence with no fluff. It front-loads the core purpose and wastes no words, which is ideal for quick comprehension.

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

Completeness3/5

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

The tool is moderately simple and the schema covers all parameters, but there is no output schema and no annotations, so return format and behavioral expectations are unclear. The description alone is adequate for basic understanding but leaves gaps around what the extraction result actually looks like.

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 the schema fully documents all three parameters. The description adds no additional semantic context beyond the schema, so it meets the baseline without further enhancement.

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 uses a specific verb ('Extract') and resource ('subtitles and captions from a YouTube video'), making the tool's function immediately clear. It is naturally distinguished from siblings like get-video-info and get-top-comments by the explicit focus on subtitle/caption extraction.

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

Usage Guidelines2/5

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

There is no guidance about when to use this tool versus alternatives, such as get-video-info or get-top-comments. The usage context is only implied by the tool name and description; no explicit exclusions or alternative routing are provided.

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