like_post

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

Discloses side effects: followers see it, matchawards records it, and idempotency. With no annotations, this fully covers behavioral traits.

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?

Three concise sentences, each adding value: purpose, use case, and idempotency. No wasted 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 simple tool with one parameter and existing output schema, the description adequately covers behavioral semantics and usage.

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 has 0% coverage and description does not elaborate on the parameter format. However, the single parameter 'post_id' is self-explanatory from context.

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?

Clearly states 'Favourite an opportunity' and contrasts with alternative actions like comment, share, and unlike, indicating a lightweight signal. Provides distinct purpose.

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?

Explicitly suggests using when 'this is relevant' and without comment overhead, providing a clear use case. Does not name alternatives but implies context.

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