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tuitamogamer-gpt

youtube-mcp-server

Rate Video

youtube_rate_video
Idempotent

Rate a YouTube video by liking, disliking, or removing your existing rating. Specify the video ID and desired rating to apply.

Instructions

Like, dislike, or remove your rating from a video.

Calls videos.rate. The rating applies to the authenticated user's account.

Args

  • videoId (string, required): video to rate

  • rating ("like"|"dislike"|"none", required): the rating to apply

Returns Short confirmation of the rating applied.

Examples

  • Like: { "videoId": "dQw4w9WgXcQ", "rating": "like" }

  • Remove rating: { "videoId": "dQw4w9WgXcQ", "rating": "none" }

Errors

  • 400: invalid rating value

  • 403: scope missing or video not accessible

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
videoIdYesID of the video to rate.
ratingYesRating to apply: "like", "dislike", or "none" (removes existing rating).
Behavior5/5

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

The description goes beyond annotations by disclosing the underlying API call (videos.rate), the behavior of applying or removing ratings, and error conditions. Annotations indicate idempotent and non-destructive behavior, which is consistent. The description adds clarity on the return value and authentication context.

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?

The description is well-structured with sections for description, args, returns, examples, and errors. It is informative but could be slightly more concise. The most important information is front-loaded.

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 tool with two parameters and no output schema, the description covers all necessary aspects: inputs, behavior, return value, examples, and errors. No information gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents the parameters. The description adds value with an 'Args' section restating parameters, examples showing concrete usage, and clarifying that 'none' removes the rating. This reinforces understanding beyond the 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 clearly states the tool's purpose: to like, dislike, or remove a rating from a video. The title 'Rate Video' aligns with this. Among many sibling tools for various YouTube actions, this is the only one for rating, so it is well-distinguished.

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

Usage Guidelines3/5

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

The description indicates the rating applies to the authenticated user's account, providing context. However, it does not explicitly state when to use this tool versus alternatives, nor when not to use it. The examples help clarify usage but lack explicit usage boundaries.

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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