lookup_labor_rates

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

Annotations already declare readOnlyHint, destructiveHint=false, and idempotentHint=true. The description adds valuable behavioral details: it specifies the output includes 'GSA CALC awarded ceiling rates (min/median/max by category) plus BLS OEWS wage data with a +55% government wrap estimate,' and notes the data is free and public. No contradictions.

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 four sentences long, with the primary purpose in the first sentence. Every sentence adds value: what it does, what it returns, that it is free/public, and a usage tip. No extraneous 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 output schema exists, the description does not need to detail the return structure fully. It covers the key output components and data sources. It lacks mention of data freshness or limitations, but for a public-data lookup tool, this is acceptable.

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% with both parameters having descriptions. The tool description does not add significant new meaning beyond the schema; it only restates that the tool works with NAICS code and/or labor category. Thus, the baseline score of 3 applies.

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 starts with 'Benchmark federal labor rates for a NAICS code and/or a specific labor category,' which clearly specifies the verb (benchmark/lookup) and resource (federal labor rates). The tool name and description align, and the sibling tools (e.g., analyze_award_patterns, predict_recompete) are distinct, so differentiation is inherent.

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 context: it is for benchmarking federal labor rates using GSA CALC and BLS data, and advises to 'verify allowable rates against the RFP.' Although it does not explicitly state when not to use or name alternatives, the context is clear and sufficient given the absence of closely related sibling tools.

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