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get_video_comments

Fetch top-level comments for a YouTube video, returning author, text, like count, reply count, and publish date. Supports relevance or time sorting.

Instructions

Fetch top-level comments from a YouTube video. Returns author, text, like count, reply count, and publish date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesYouTube video URL or video ID
orderNoSort order (default: relevance)
max_resultsNoNumber of comments (1-100, default: 20)
Behavior4/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Fetch' clearly signals a read-only, non-destructive operation, and the description further discloses the exact return fields (author, text, like count, reply count, publish date). It does not cover authentication, rate limits, or errors, but these are secondary for this simple read tool.

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?

Two sentences with no filler or redundant content. The action and scope are front-loaded, and the return fields are listed concisely. Every sentence serves a clear purpose.

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 low-complexity tool with fully documented parameters and a returning output field list, the description covers the essential information an agent needs. It could note pagination or API-specific quirks, but given the simple scope and clear schema, the description is sufficiently complete.

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 all three parameters (url, order, max_results) are already documented in the schema. The description adds no extra parameter-level detail, but it does not need to; the baseline of 3 applies because 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 uses a specific verb ('Fetch') with a clear resource ('top-level comments from a YouTube video'), and explicitly scopes to top-level comments, which distinguishes it from sibling video retrieval tools that return video metadata or search results. It is unambiguous and immediately tells an agent what the tool does.

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 intended usage is clear: use this tool when you need comments for a specific YouTube video. It does not explicitly name sibling tools or state exclusion conditions, but the description's focus on comments makes the usage context obvious enough among the likely alternatives.

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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