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

Annotations already cover read-only/idempotent/non-destructive behavior, and the description adds useful output semantics: the result is aggregated across 'every matching contract rate' and contains 'no individual rows'. It does not discuss edge cases like empty results, but this is minor given the strong annotation coverage.

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?

Three sentences front-load the core distinction (statistics vs listings), provide usage guidance, and give a concrete example. There is no filler or repeated schema content.

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 11 parameters and no output schema, the description covers the return shape, aggregation behavior, and use case. One minor gap is that sort_by/sort_dir appear in the schema but are not addressed relative to the stated 'no individual rows' behavior, which could confuse an agent.

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?

The schema covers 100% of parameters with individual descriptions, so the baseline is 3. The description adds an example call and the pointer 'Same filters as gsa_labor_rates', but it does not explain each parameter beyond what the schema already provides.

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 operation and resource: 'Price statistics only for a GSA schedule labor category' and enumerates exact outputs (min, 25th percentile, median, 75th percentile, max). It differentiates itself from sibling gsa_labor_rates by explicitly saying 'with no individual rows' and 'rather than the listings'.

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?

It provides an explicit conditional: 'Use when you want the benchmark number rather than the listings', plus two natural-language query examples. It also points to the sibling tool's filter semantics ('Same filters as gsa_labor_rates'), giving the agent a clear selection rule.

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