get_linkedin_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. It only says 'normalized details' but does not disclose what normalization entails, potential error conditions, authentication requirements, or rate limits. This leaves significant behavioral ambiguity.

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, concise sentence that directly states the tool's purpose. There is no unnecessary verbiage, and it is appropriately front-loaded.

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?

For a simple tool with one parameter and an output schema, the description covers the basics, but it lacks clarity on what constitutes a 'public' job fails or whether there are any restrictions. The output schema likely covers return values, but the absence of any behavioral or usage context makes it only minimally complete.

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 single parameter 'job_id_or_url' is self-explanatory to some degree, but schema description coverage is 0% and the description adds nothing about accepted formats, examples, or the difference between an ID and a URL. The agent must infer these details from the parameter name alone.

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 ('normalized details for one public LinkedIn job'), which distinguishes it from search and raw-payload tools. However, it does not explicitly contrast with sibling tools within the description itself, so it falls just short of 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?

Usage context is implied: use this tool to fetch details for a single job. But there is no explicit guidance on when to use this instead of search_linkedin_jobs or get_linkedin_job_raw_payload, nor any exclusions or alternatives mentioned.

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