get_user_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. It discloses pagination via cursor and time bounds, but lacks information on rate limits, authentication requirements, or behavior when a user does not exist. This is adequate but leaves some behavioral gaps.

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 with three sentences. The first sentence states the core purpose, the second adds use cases, and the third covers technical details (time and pagination). No superfluous content; it is front-loaded and efficient.

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 simplicity (4 parameters, no output schema, no nested objects), the description is reasonably complete. It covers purpose, use cases, and parameter mechanics. However, it does not describe the return format or error handling, which would be beneficial.

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 coverage is 100%, so the baseline is 3. The description adds minor context to parameters (e.g., 'Unix seconds' for times, 'without @' for userName) but does not significantly enhance understanding beyond the schema descriptions.

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: 'Fetch tweets that mention a specific Twitter/X user.' It uses a specific verb ('Fetch') and resource ('tweets mentioning a user'), and provides concrete use cases like brand monitoring and sentiment tracking. This effectively distinguishes it from sibling tools such as search_tweets or get_user_last_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?

The description provides clear context for when to use this tool, citing brand monitoring and conversation tracking. It mentions time-bound queries and pagination, which are useful hints. However, it does not explicitly state when not to use it or mention alternatives like search_tweets for broader searches.

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