get_tweet_metrics

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 the metrics returned (likes, retweets, replies, impressions), which is helpful. However, it omits details about rate limits, authentication needs, or whether the operation is read-only (though it's implied).

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 a single, well-structured sentence that conveys all necessary information without unnecessary words.

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 low complexity (1 parameter, no output schema), the description is complete enough for an AI agent to understand what the tool does and what it returns.

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 already describes tweet_id comprehensively (100% coverage). The description adds no new semantic information about the parameter beyond 'specific tweet', which is already clear from the schema.

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: retrieving engagement metrics for a specific tweet, listing specific metrics like likes, retweets, replies, and impressions. This distinguishes it from siblings like get_mentions or search_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 implies usage when a tweet's engagement data is needed, but does not explicitly state when to use this tool versus alternatives like get_mentions (which focuses on mentions) or get_replies (which focuses on replies). No exclusion criteria are given.

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