list_channel_posts_pollvideoimage

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. It mentions 'pagination scraping is supported,' which adds some context about handling large data sets. However, it lacks details on rate limits, authentication needs, error conditions, or what the output looks like (since no output schema exists). For a tool with no annotations, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded, stating the core purpose in the first sentence. The second sentence adds useful context about pagination. There's no wasted text, and both sentences earn their place by providing distinct information.

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

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

Given the tool's complexity (3 parameters, no annotations, no output schema), the description is incomplete. It lacks details on output format, error handling, authentication, and usage scenarios. Without an output schema, the description should ideally hint at return values or structure, but it doesn't. This leaves significant gaps for an agent to understand the tool fully.

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

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

The input schema has 100% description coverage, so the schema already documents all parameters (channelId, lang, nextToken). The description doesn't add any meaning beyond what's in the schema—it doesn't explain parameter interactions, defaults, or usage examples. With high schema coverage, the baseline score is 3, as the description doesn't compensate but also doesn't detract.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'lists poll, video, or image posts of a YouTube channel.' It specifies the resource (YouTube channel posts) and content types (poll, video, image). However, it doesn't explicitly differentiate from sibling tools like 'list_channel_videosshortslive' or 'get_post_details,' which reduces clarity about when to use this specific tool.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions 'pagination scraping is supported,' which hints at usage for large datasets, but doesn't specify scenarios, prerequisites, or exclusions. Without explicit when/when-not instructions or named alternatives, users must infer usage from the purpose alone.

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