LinkedIn person profile

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 only lists the data fields (headline, experience, education, skills) and implies structured output, but does not disclose whether the tool performs a live scrape, handles private profiles, the format of the response, potential errors, or any access requirements. This is insufficient for a tool that may encounter varied LinkedIn profile states.

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, well-structured sentence that is front-loaded with the key term 'Structured LinkedIn person profile data.' It lists the main fields concisely, contains no fluff, and every word adds value. It is an exemplar of efficient, lean tool description for a simple data-fetching tool.

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 provides the core information: what data is returned. However, it lacks context about potential pitfalls such as LinkedIn's login walls, rate limits, or how to handle invalid URLs. It is minimally viable but leaves gaps that could affect correct invocation in edge cases.

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 already describes the single parameter 'url' as 'LinkedIn person profile URL' with a uri format, giving 100% schema coverage. The description adds no extra parameter-level semantics (e.g., URL format requirements, public-profile constraints). Thus, it does not enhance what the schema already provides, but it also does not need to—the schema is clear. Baseline of 3 is appropriate.

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 states that the tool provides 'Structured LinkedIn person profile data' and lists specific fields (headline, experience, education, skills). This clearly indicates the tool's purpose and distinguishes it from sibling tools like web_data_linkedin_company_profile and web_data_linkedin_posts. However, it lacks an explicit verb such as 'fetches' or 'returns,' making it slightly less directive than a full action-oriented description.

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 does not provide any guidance on when to use this tool versus alternatives. It only describes the output content. The sibling tool names imply usage (e.g., person profile vs. company profile), but the description itself offers no explicit when-to-use or when-not-to-use instructions, leaving the agent to infer context solely from the tool name.

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