get_number_of_likes

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 states the tool returns an integer count, which is helpful, but lacks details on error handling, rate limits, authentication needs, or data freshness. For a read operation with no annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond basic output.

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 extremely concise and front-loaded, with three short sentences that directly state the purpose, input, and output without any wasted words. Each sentence earns its place by providing essential information, making it easy 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 low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and parameter semantics but lacks behavioral details and usage guidelines. For a simple read tool, this might suffice, but it doesn't fully address potential agent needs like error scenarios or sibling differentiation.

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 description adds meaningful context beyond the input schema, which has 0% description coverage. It specifies that the input is 'post_id (str)' and clarifies the parameter's role in fetching likes for a post. Since there's only one parameter, the description effectively compensates for the schema's lack of detail, though it doesn't elaborate on format or validation rules.

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: 'Return the number of likes on a post.' It specifies the verb ('Return') and resource ('number of likes on a post'), making the function unambiguous. However, it doesn't explicitly differentiate from siblings like 'get_post_reactions_like_total' or 'get_post_insights', which might offer overlapping or related data, so it doesn't reach 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 no guidance on when to use this tool versus alternatives. It doesn't mention siblings like 'get_post_reactions_like_total' (which might provide similar like counts) or 'get_post_insights' (which could include likes among other metrics), nor does it specify prerequisites or exclusions. Usage is implied by the purpose but not explicitly defined.

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