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Video comments

youtube_get_comments
Read-onlyIdempotent

Fetch top-level comments from a YouTube video and extract audience signals such as questions and most-liked responses to support audience analysis.

Instructions

Top-level comments on a video, with quick derived signals (questions, most-liked) for audience analysis.

Input Schema

NameRequiredDescriptionDefault
orderNo'relevance' (top) or 'time' (newest)relevance
video_idYesYouTube video ID
channel_idNoYouTube channel ID (UC...). Omit to use the default connected channel.
max_resultsNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.5

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare read-only, idempotent, non-destructive, open-world behavior, so the bar is lower. The description does add real scope information beyond annotations: only top-level comments are returned (replies excluded) and extra computed fields (questions, most-liked) are attached. It says nothing about pagination, the 100/300 result cap, or quota behavior.

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?

A single well-formed sentence with the most important qualifier ('top-level') front-loaded and no filler. It is efficient, though arguably under-specified rather than tight.

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?

An output schema exists, so return values need not be explained. However, for a tool with four parameters and a default result order that materially changes what an agent sees, the description offers no guidance on ordering, limits, or channel scoping, leaving gaps the schema does not fill.

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 coverage is 75%: order, video_id and channel_id carry descriptions, while max_results only has bounds and a default. The description adds no parameter meaning at all, so it neither compensates for the coverage gap nor clarifies enum/default behavior. Baseline 3 is appropriate given the schema does most of the work.

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?

Names the resource precisely (top-level comments on a video) and scopes it to non-reply comments, plus flags derived signals (questions, most-liked). The retrieval verb is only implied by the tool name rather than stated, and no sibling tool is named, but the resource is unmistakable.

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?

The only usage hint is the trailing phrase 'for audience analysis', which implies intent but never states when to call this versus other video/analytics tools or any prerequisites (e.g. needing a video_id from youtube_list_videos). No exclusions or alternatives are given.

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