get_mentions

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 the tool is 'useful for tracking UGC,' which hints at a read-only, monitoring purpose, but fails to specify critical behaviors like whether it requires authentication, rate limits, pagination, or the format of returned posts. This is inadequate for a tool with no annotation support.

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 highly concise and front-loaded: two sentences that directly state the purpose and a brief use case. Every word earns its place without redundancy or fluff, making it easy for an agent to parse quickly.

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 (fetching mentions, likely from a social media API), lack of annotations, and no output schema, the description is incomplete. It doesn't cover authentication needs, rate limits, error handling, or the structure of returned posts. While concise, it fails to provide enough context for reliable agent use, especially compared to siblings with similar data retrieval functions.

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, with the 'limit' parameter fully documented (type, range, default). The description adds no parameter-specific information beyond what the schema provides, such as how 'limit' affects results or any implicit defaults. Given the high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.

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: 'Get posts where your account has been tagged or @mentioned.' It specifies the verb ('Get') and resource ('posts'), and distinguishes itself from siblings like 'get_comments' or 'get_media_posts' by focusing on mentions. However, it doesn't explicitly differentiate from 'get_conversations' or 'get_comments' in terms of mention-specific filtering, which prevents a perfect score.

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 implied usage guidance with 'Useful for tracking UGC,' suggesting it's for monitoring user-generated content mentions. However, it lacks explicit when-to-use rules, alternatives (e.g., vs. 'get_comments' for general comments), or exclusions. This leaves gaps in distinguishing it from similar tools, resulting in a minimal viable score.

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