get_linkedin_post_reposts

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 mentions retrieving reposts but does not cover critical aspects like rate limits, authentication requirements, error handling, or the format of returned data. This leaves significant gaps in understanding how the tool behaves in practice.

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, direct sentence that efficiently conveys the core purpose without unnecessary words. It is front-loaded and wastes no space, 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 lack of annotations and output schema, the description is incomplete. It does not address behavioral traits, return values, or usage context, which are essential for a tool that interacts with an external API like LinkedIn. This leaves the agent with insufficient information to use the tool effectively beyond basic parameter passing.

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 description coverage is 100%, meaning all parameters are documented in the input schema. The description adds no additional semantic context beyond implying the 'urn' parameter specifies the post. Since the schema handles parameter details adequately, the baseline score of 3 is appropriate, as the description does not compensate or add value beyond the schema.

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 ('Get LinkedIn reposts') and the target resource ('for a post by URN'), making the purpose understandable. However, it does not explicitly differentiate from sibling tools like 'get_linkedin_post_reactions' or 'get_linkedin_post_comments', which also retrieve post-related data but for different aspects.

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 other LinkedIn post-related tools (e.g., for reactions or comments). It lacks context about prerequisites, limitations, or scenarios where this tool is preferred, leaving usage decisions ambiguous.

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