unlike_tweet

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'Remove a like from a tweet by id,' which implies a mutating operation but does not mention potential errors (e.g., if the tweet is not liked), authentication requirements, side effects, or whether the operation is reversible. The minimal wording leaves significant behavioral aspects undisclosed.

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, concise sentence that gets straight to the point. It contains no filler or redundant information, and the key verb and object are front-loaded, making it easy for an agent to quickly parse the tool's purpose.

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?

For a simple one-parameter mutation tool with no output schema, the description is functional but not fully complete. It lacks details on return behavior (e.g., success/failure responses), error handling, and prerequisites (e.g., authentication). However, given the simplicity, the description provides enough for an agent to invoke the tool correctly in most straightforward cases, though clear gaps remain.

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 schema has one parameter (tweet_id, string, required) with 0% coverage from the description. The description's phrase 'by id' adds marginal meaning by linking the parameter to a tweet identifier, but it does not compensate for the lack of schema descriptions. The parameter name 'tweet_id' is self-explanatory, so the description offers little beyond what the schema already conveys.

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 action ('Remove a like'), the target resource ('a tweet'), and the mechanism ('by id'). It distinguishes itself from sibling tools like 'like_tweet' by explicitly indicating removal, and from other tweet-related actions by focusing solely on unlike behavior.

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, such as 'like_tweet' (which adds a like) or other mutation tools. While the inverse relationship to 'like_tweet' is inferable, the description itself gives no explicit context, exclusions, or alternatives.

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