datasets_jobs_companies

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 goes beyond a bare 'searches' by explaining the data source (discovered board registry), the sponsors_visa filter semantics (DOL LCA filings), and the historical/non-guarantee nature of the data. However, it does not cover return structure, pagination, or potential staleness, so it is strong but not complete.

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 first four sentences are well-structured and front-loaded with the core purpose. However, the description then repeats the full provider, status, and sort enums that are already fully enumerated in the input schema. This redundant duplication adds length without new information, making the description less concise than it could be.

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?

There is no output schema, so the description must substitute for return-value semantics. It does hint at the main output dimensions (which companies, which ATS/platform, how many open roles) and the historical caveat, but it omits the exact return structure, pagination behavior, and how to handle empty results. For a search tool with 8 optional parameters, this is adequate but not fully 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?

Schema coverage is 100%, so the baseline is 3. The description adds real meaning beyond the schema by explaining the sponsors_visa parameter in depth ('certified employer filings in recent public U.S. Department of Labor LCA disclosure data') and by clarifying the provider enum's split between ATS providers and single-company big-tech platforms. This helps an agent understand which parameter to set and what the enum values represent.

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 a specific verb and resource: 'Find which companies are hiring' via the 'discovered company board registry.' It distinguishes itself from job-level or item-level siblings by explicitly describing this as company-level data and 'not a guarantee for a specific role or candidate.' This is specific enough for an agent to understand what this tool does and what it does not do.

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 gives implied usage boundaries: it is company-level historical evidence, so an agent can infer it is not for specific role lookups. However, it never names alternatives such as datasets_jobs_item or jobs_company_search, and it does not explicitly say when to choose this tool over those siblings. The sponsors_visa=true explanation is a useful condition, but not a routing guideline.

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