get_job

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It mentions 'Returns cleaned data in TOON format', which adds some behavioral context about output transformation. However, it lacks critical details like whether this is a read-only operation, authentication requirements, rate limits, or error handling—important for a tool interacting with external APIs like LinkedIn.

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 very concise—two short sentences that are front-loaded with the core purpose. There's no wasted text, and it efficiently communicates key actions and outputs. However, it could be slightly more structured by explicitly separating purpose from output details for even clearer scanning.

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 no annotations and no output schema, the description is incomplete for a tool with external API interactions. It mentions TOON format but doesn't explain what that entails, and omits behavioral aspects like data sourcing, error cases, or usage constraints. For a tool fetching LinkedIn data, this leaves significant gaps for an agent to operate safely and effectively.

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%, so the schema already documents all three parameters (jobId, url, save_dir) adequately. The description adds no parameter-specific information beyond what's in the schema, such as clarifying relationships between jobId and url, or explaining TOON format implications for save_dir. Baseline 3 is appropriate when schema does the heavy lifting.

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 LinkedIn job details' specifies the verb (get) and resource (job details), and 'Returns cleaned data in TOON format' adds useful output context. However, it doesn't explicitly differentiate from sibling tools like 'search_jobs' or 'get_post', which would require a 5.

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. With siblings like 'search_jobs' available, there's no indication whether this is for retrieving specific known jobs versus searching, or any prerequisites for usage. This leaves significant ambiguity for an agent.

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