datasets_jobs_search

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

With no annotations to rely on, the description carries the full burden and does so thoroughly. It discloses how salary is parsed, that min/max salary comparisons require a common currency, that location filters match parsed fields rather than substrings, that job_family excludes unclassified postings, and that employment_type and posted_at are never populated for certain providers. These are critical behavioral details beyond the schema.

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 dense but well-organized, starting with purpose, then scope, defaults, and detailed filtering semantics. Each sentence adds value, and the enum lists and provider caveats are clearly delineated. Despite its length, the structure makes it easy to scan.

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 has 19 parameters, no annotations, and no output schema, the description is remarkably complete. It covers default behavior, filtering semantics, salary and location edge cases, job_family classification, provider-specific gaps, and enum values, giving an agent everything needed to invoke the tool correctly.

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?

Although the schema already describes each parameter, the description adds significant semantic meaning: min_salary/max_salary operate on parsed salary and require salary_currency, city/state/country are exact-match on parsed components, include_closed defaults to false to include open roles, and the provider enum is listed with limitations for specific providers. This goes well beyond the schema descriptions.

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 identifies the tool as 'Search the jobs dataset (all companies' live postings)' with a specific verb and resource, and distinguishes it from sibling tools by noting it covers 'every job posting crawled from every discovered company ATS board' plus major tech career platforms. This sets it apart from provider-specific job board tools and other dataset search tools.

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 strong usage context: open roles are the default, include_closed can be set for historical roles, salary filters require salary_currency, and location filters use exact parsed components. It does not explicitly name alternative tools for when this one should not be used, but the scope ('all companies') implies the distinction.

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