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

Annotations already establish readOnlyHint and non-destructive behavior, which the description correctly does not contradict. The description adds meaningful behavioral context beyond annotations: output percentiles, state/industry adjustments, 50+ role families, data source freshness, and a per-call cost. It does not mention rate limits or error behavior, but the provided context is strong for a read-only benchmark 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 front-loaded with use cases and output, followed by a concrete example, source, and cost. It is informative without being bloated, though the retention statistic is slightly promotional and could be trimmed. Overall, every sentence earns its place.

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 relatively simple benchmark tool, the description covers the key operational facts: when to use it, what it returns, the adjustments available, the data source, and pricing. There is no output schema, but the percentiles and example give adequate expectations. Minor gaps remain around exact matching of job titles and response structure, but these are not critical.

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 100%, so the schema already documents job_title, state, and industry with examples. The description's mention of 'state and industry adjustments' reinforces the schema but adds no new parameter-level semantics. Baseline 3 is appropriate.

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: returning salary benchmarks (p25/p50/p75) with state and industry adjustments for any role. It names specific use cases and gives a concrete example. However, it does not explicitly differentiate itself from the many sibling get_*_benchmark tools, so it misses the 'distinguishes from siblings' bar for a 5.

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 explicit when-to-use context: setting compensation ranges, evaluating job offers, and preparing comp committee presentations. It does not mention when not to use the tool or suggest alternative sibling tools, which prevents a 5.

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