get_company_posts

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 what the tool does but lacks details on behavioral traits such as rate limits, authentication requirements, pagination, data freshness, or error handling. For a tool that likely accesses external APIs (LinkedIn), 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 appropriately sized and front-loaded, with the core purpose stated concisely in the first sentence. The 'Args' section adds necessary parameter details without redundancy. However, the structure could be slightly improved by integrating the parameter explanation more seamlessly, but overall it's efficient with minimal waste.

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 (accessing LinkedIn data), no annotations, and an output schema present (which handles return values), the description is minimally adequate. It covers the purpose and parameter semantics but lacks usage guidelines and behavioral transparency, leaving gaps in context that could hinder effective tool selection and invocation by an AI agent.

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 for the single parameter 'company_name' by specifying it as a 'LinkedIn company name' and providing examples ('google', 'stripe', 'openai'). Since schema description coverage is 0% and there's only one parameter, this effectively compensates for the schema's lack of details, making the parameter semantics clear and actionable.

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 resource 'recent posts from a company's LinkedIn feed', making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from sibling tools like 'get_company_profile' or 'search_people', which might also retrieve LinkedIn data but for different resources.

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 scenarios where this tool is preferred over siblings like 'get_company_profile' (which might get company info instead of posts) or 'search_people' (which might find individuals). There's no context on prerequisites, limitations, or exclusions for usage.

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