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mohamdben-yahia

YouTube MCP Server

get_video_comments

Retrieve top-level comments and discussion threads for a YouTube video, sorted by relevance or newest, with pagination support for browsing all comments.

Instructions

Retrieve top-level comment threads and discussions for a video.

Args: video_id: Video ID or URL to fetch comments for. max_results: Number of comments to retrieve (up to 100, default 20). order: Sort order: 'relevance' (most popular) or 'time' (newest first). page_token: Token for retrieving next page of comments. raw: If True, returns unaltered raw YouTube Data API response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rawNo
orderNorelevance
video_idYes
page_tokenNo
max_resultsNo

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

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It disclosures key behavior: returns top-level comments, supports relevance/time ordering, paginates via page_token, and includes a raw mode for the unaltered API response. It does not mention auth or rate limits, but the available output schema covers return format, so this is adequate.

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 tight: a one-sentence summary followed by a labeled Args block. Every line adds new information, and the structure makes parameter details easy to scan.

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

Completeness5/5

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

Given that an output schema exists, the description correctly focuses on parameters and behavior. It covers all five parameters, defaults, and important variations. For this complexity level and with no annotations, it is complete enough for an agent to invoke the tool accurately.

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%, so this description fully compensates: every parameter is documented with its allowed values, defaults, and constraints (e.g., max_results cap of 100, order options, page_token purpose, raw behavior). This goes far beyond the bare input 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 uses a specific verb, 'Retrieve', with a clear resource: 'top-level comment threads and discussions for a video'. It is unambiguous and distinct from all sibling tools, none of which deal directly with video comments.

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 clearly identifies the target case—fetching comments for a video—and gives param-level context. It does not explicitly compare against alternatives, but no sibling tool overlaps with comment retrieval, so the usage context is sufficiently 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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