get_linkedin_post_reactions

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 action is to 'Get' reactions, implying a read-only operation, but does not disclose any behavioral traits such as rate limits, authentication needs, pagination, or what the return format looks like (e.g., list of reactions with details). For a tool with no annotations, this is a significant gap in transparency.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized, with every part contributing to clarity, making it highly concise and well-structured.

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 moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral aspects, usage context, and return values. With no output schema, the description should ideally hint at the response format, but it does not, leaving gaps in completeness for effective agent use.

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 has 100% description coverage, with clear documentation for 'count', 'timeout', and 'urn' parameters. The description adds no additional meaning beyond the schema, such as explaining URN format constraints or usage tips. Since schema coverage is high, the baseline score is 3, as the description does not compensate but also does not detract.

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 'Get' and the resource 'LinkedIn reactions for a post by URN', specifying it retrieves reactions for a LinkedIn post using a URN. However, it does not distinguish this from sibling tools like 'get_linkedin_post_comments' or 'get_linkedin_user_reactions', which handle related but different data types, so it lacks explicit sibling differentiation.

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 'get_linkedin_post_comments' for comments or 'get_linkedin_user_reactions' for user-specific reactions. It mentions the URN requirement but does not clarify usage context, exclusions, or prerequisites, leaving the agent to infer based on tool names alone.

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