twitter_liking_users

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

No annotations provided. The description describes the return format but does not disclose behavioral traits such as authentication needs, rate limits, or that it's a read operation.

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 very concise at two sentences, but it is incomplete. It includes both purpose and return type but lacks parameter details and usage context.

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?

With 3 parameters and no output schema, the description should explain parameters and use cases. It only covers the response format, leaving important details like what 'id' refers to and how to filter results.

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?

Schema description coverage is 0%, and the description does not explain any of the three parameters (id, max_results, user_fields). It only describes the response structure, leaving the agent to infer parameter semantics.

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 retrieves users who liked a given post. The verb 'liked' and resource 'users' are specific, but it does not differentiate from sibling tools like twitter_retweeted_by or twitter_quote_tweets.

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?

No guidance on when to use this tool versus alternatives like twitter_retweeted_by or twitter_quote_tweets. The description provides no context for when it's appropriate.

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