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mohamdben-yahia

YouTube MCP Server

get_video_details

Retrieve metadata, view counts, likes, duration, and tags for up to 50 YouTube videos by providing video IDs or URLs.

Instructions

Retrieve metadata, views, likes, duration, and tags for one or more YouTube videos.

Args: video_ids: List of 11-character video IDs or URLs (up to 50 IDs). raw: If True, returns unaltered raw YouTube Data API response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rawNo
video_idsYes

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

A4.1/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral disclosure burden. It discloses that raw=true returns the unaltered YouTube Data API response and that up to 50 IDs are accepted, which is useful added context. However, it does not mention error handling, rate limits, or authentication expectations, leaving some behavioral gaps.

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 concise and well-structured: one clear sentence describing the tool's purpose followed by a compact Args section. Every sentence adds useful information with no filler.

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 simple retrieval tool with two parameters and an output schema, the description is largely complete: it covers input format, limits, and the raw-response behavior. It does not address rate limits or auth, but these are not likely required for correct invocation given the tool's simplicity and existing output schema.

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?

The input schema only defines types, but the description adds critical semantics: video_ids can be 11-character IDs or URLs, up to 50 IDs, and raw controls whether the response is unaltered. This fully compensates for the 0% schema description coverage and goes well beyond the bare schema.

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?

The description clearly states the tool retrieves metadata, views, likes, duration, and tags for one or more YouTube videos, with a specific verb and resource. It is clear but does not explicitly differentiate from sibling tools like get_video_transcript or get_video_comments, so it stops short of a 5.

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 gives clear context: use this when you need metadata for specific YouTube video IDs or URLs, up to 50 at a time. It does not state when-not-to-use or name alternatives, but the purpose is specific enough that an agent can select it appropriately.

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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