top_companies

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

With no annotations, the description carries the full burden. It discloses the key behavioral trait of returning only the top 5 employers and explains filter semantics (e.g., AND matching for keywords). It also provides helpful cross-references for parameter discovery. Some details like output structure or edge-case handling are omitted, but the description is adequate for a read-only aggregation tool.

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 concise and well-structured: a one-sentence purpose, followed by a compact bullet list of parameters. Every sentence provides useful information, with no redundancy or wasted words.

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 simplicity (4 params, 1 required, no output schema), the description covers all necessary input semantics and examples. It could be slightly more explicit about return format (e.g., a list of company names with vacancy counts), but the title and first sentence already convey the core output. Overall, it is sufficiently complete for an agent to use 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?

Schema has 0% description coverage, but the tool description fully compensates by explaining every parameter with concrete examples: country codes ('gb', 'us', 'de'), keyword AND semantics, category source, and hierarchical location arrays. This adds significant meaning beyond the raw schema.

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 function: 'Get a leaderboard of the top 5 employers by number of vacancies.' This is a specific verb+resource combination that distinguishes it from siblings like search_jobs or salary_histogram.

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 clear context for when to use the tool, specifying required (country) and optional filters (what, category, location) with examples. It also references related tools (list_categories, regional_data) for discovering valid values. However, it lacks explicit exclusions or alternatives, so it does not fully meet the 'when not to use' criterion.

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