Skip to main content
Glama
Sign In

YouTube Transcript AI

by ai.youtube-transcript
OverviewDiscussionsAdmin

get_video_comments

Fetch the top comments of a YouTube video (YouTube's "top" ordering, replies not included), with like counts. Comments are where viewers report what worked for them, which makes them useful for checking claims made in the video.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "video": "<video>"
    },
    "name": "get_video_comments"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoHow many comments. Default 30.
videoYesYouTube video URL or 11-character video ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does disclose key traits: comments use YouTube's top ordering, replies are excluded, and like counts are included. It does not cover auth or rate-limit behavior, but for this read-only fetch tool the disclosure is strong.

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 two sentences with the core behavior front-loaded. The second sentence provides a genuine rationale for using comments rather than repeating structured fields, so every sentence earns its place.

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?

For a simple two-parameter read tool with a fully described input schema and no output schema, the description is complete. It covers what is fetched, how comments are ordered, what is excluded, and what metadata is returned.

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 both the video and limit parameters are already documented by the schema. The description adds no extra parameter-level detail, which meets the baseline when the schema does the heavy lifting.

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 states a specific verb and resource: fetch the top comments of a YouTube video. It also scopes the result by naming YouTube's top ordering, excluding replies, and including like counts, so an agent knows exactly what this tool returns.

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 a clear use case: comments show what worked for viewers, making them useful for checking claims made in the video. It does not explicitly name alternatives or exclusions, but the context is sufficient for an agent to decide when this tool is appropriate.

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

All YouTube Transcript AI toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources