get_job_details

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 tool retrieves details but doesn't mention aspects like rate limits, authentication needs, data freshness, or error handling. For a read operation with no annotation coverage, 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 front-loaded with the core purpose in the first sentence, followed by a concise 'Args' section that adds necessary parameter details without redundancy. Every sentence earns its place, and there's no wasted text, making it highly efficient 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 low complexity (1 parameter), no annotations, but an output schema exists, the description is reasonably complete. It explains what the tool does and the parameter meaning, and the output schema will handle return values. However, it could improve by addressing behavioral aspects like permissions or limitations, which are missing.

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 schema description coverage is 0%, so the description must compensate. It adds meaningful context for the single parameter 'job_id' by explaining it's a 'LinkedIn job ID' and providing examples (e.g., '3912045678'), which clarifies the format beyond the schema's basic string type. This effectively covers the parameter semantics, though it doesn't detail constraints like length or validation.

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 tool's purpose: 'Get job details for a specific job posting on LinkedIn.' It specifies the verb ('Get') and resource ('job details'), and distinguishes it from sibling tools like 'search_jobs' by focusing on a specific job rather than searching. However, it doesn't explicitly contrast with 'get_company_profile' or 'get_person_profile' in terms of data scope, which prevents a perfect score.

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 implies usage by specifying 'for a specific job posting,' suggesting it should be used when a job ID is known, unlike 'search_jobs' for broader queries. However, it lacks explicit guidance on when not to use it (e.g., vs. 'get_company_posts' for company-related content) or clear alternatives, making it only moderately helpful.

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