get_post_reactions_like_total

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 states it 'fetches' data, implying a read-only operation, but doesn't mention any behavioral traits such as authentication needs, rate limits, error handling, or whether it's safe to call repeatedly. This leaves significant gaps in understanding how the tool behaves beyond its basic function.

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 only three sentences that directly state the purpose, input, and output without any wasted words. Every sentence earns its place by providing critical information efficiently.

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, no annotations), the description is minimally adequate but incomplete. It covers the basic purpose and parameters but lacks usage guidelines, behavioral details, and output specifics, making it insufficient for fully informed tool selection without additional inference.

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 by specifying that the input is 'post_id (str)' and the output is 'dict with like count', which clarifies the parameter type and return format. Since schema description coverage is 0%, this compensates well by providing essential semantics not covered in the schema, though it doesn't detail the structure of the output dict beyond the count.

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 verb 'Fetch' and the resource 'number of "Like" reactions', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_number_of_likes' or 'get_post_reactions_anger_total', which might cause confusion about when to use this specific tool versus others.

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 like 'get_number_of_likes' or other reaction-type tools in the sibling list. It lacks context about prerequisites, exclusions, or specific scenarios where this tool is preferred, leaving the agent to infer usage from the name alone.

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