search_linkedin_jobs

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 disclosing behavior. It describes the basic action but omits important details such as legal implications of scraping, rate limits, authentication requirements (if any), and error handling. The phrase 'scrape' hints at potential issues but is not elaborated.

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 extremely concise: one line for purpose, then a structured args list. Every sentence serves a purpose with no wasted words. The most important information (what the tool does) is 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 tool with 3 parameters and no annotations, the description covers the essential inputs. An output schema exists, so return values need not be explained. However, given the scraping nature, additional context about ethical use or limitations would improve completeness. Overall, it is adequate for a straightforward tool.

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 0%, meaning the JSON schema provides no property descriptions. The description compensates by explaining each parameter: query as 'Job title or keywords' with example, location as 'Location filter' with example, and max_pages as 'Pages of results to scrape (default 1).' This adds meaningful context beyond type and name.

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 'Search LinkedIn job postings,' which is a specific verb ('search') and resource ('LinkedIn job postings'). This distinguishes it from sibling tools like 'search_github' (repos) and 'search_books' (books).

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 parameter examples (e.g., 'machine learning engineer' for query, 'London' for location) and mentions the default for max_pages, giving the agent a clear sense of usage. However, it does not explicitly state when to use this tool versus alternatives like 'search_github' or 'search_books,' though the name and resource difference make it implicit.

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