get_url_for_jobs_search

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 adequately describes the core behavior (URL generation for LinkedIn job search) and mentions compatibility with LinkedIn's API, but lacks details about rate limits, authentication requirements, error handling, or what makes the URL 'properly encoded' beyond basic encoding. It doesn't contradict any annotations.

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 well-structured with three focused sentences: purpose statement, parameter explanation, and return value. Each sentence earns its place by providing distinct information without redundancy. The Args/Returns formatting enhances clarity without unnecessary verbosity.

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 (single parameter, no annotations, no output schema), the description is mostly complete. It covers purpose, parameter meaning, and return type adequately. However, it could benefit from more behavioral context (e.g., encoding specifics, error cases) since there are no annotations or output schema to fill those gaps.

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?

With 0% schema description coverage and only one parameter, the description adds significant value beyond the input schema. It clearly explains the 'query' parameter as 'The search query string for jobs in LinkedIn,' providing essential semantic context that the schema's title ('Query') and default value alone don't convey. This fully compensates for the schema's lack of description.

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 with specific verbs ('Generates a properly encoded URL') and resource ('to search for jobs on LinkedIn'), distinguishing it from sibling tools like get_jobs_raw_metadata or get_new_job_ids which handle different aspects of job data. It explicitly mentions compatibility with LinkedIn's job search API, providing clear differentiation.

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 clear context for when to use this tool ('to search for jobs on LinkedIn'), but does not explicitly state when not to use it or name alternatives. It implies usage for URL generation rather than direct job fetching, but lacks explicit exclusions or comparisons to sibling tools like get_jobs_raw_metadata.

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