salary_benchmark

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

Annotations declare readOnlyHint=true, which covers the safety profile. The description adds behavioral details beyond that: it warns that narrow slices get 'thin fast' (small sample sizes) and states that it uses all matching postings rather than a limited subset. It does not disclose potential edge cases (e.g., invalid location format) but provides meaningful context beyond the annotation.

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 tightly structured: a one-sentence purpose, output summary, clearly labeled Args block, and a final usage recommendation. Every sentence earns its place, and the most important alternative is front-loaded in the last paragraph. No redundancy.

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 two optional params and an output schema, the description covers the essential context: what it returns, how to invoke it, and when to prefer it over a sibling. The output schema exists and likely details return fields, so the description need not do so exhaustively. The given details are sufficient for an agent to call it 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 coverage is 0%, so the description is the sole source of parameter meaning. It fully compensates: location is specified as 'US metro in "City, ST" form' with an omission default, and skill lists the exact valid values ('python, sql, excel, tableau, aws, spark') with omission behavior. This adds substantial value beyond the bare string 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 opens with a specific verb and resource: 'Get the salary distribution for a slice of the job market.' It then details the exact outputs (median, percentiles, min/max, count) and explicitly contrasts with the sibling search_jobs, making the tool's unique contribution unmistakable.

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 explicit guidance: 'Prefer this over calling search_jobs and averaging the results yourself' and explains why (uses all postings vs. max 25). It also clarifies how to scope the query via location and skill and when to omit them, giving clear conditions for use.

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