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

Annotations already indicate read-only, and the description adds meaningful context: output includes spend bands, category ranges, and outlier flags, plus a concrete example and data source. This goes beyond the minimal safety annotation and sets expectations for the response.

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 with a purpose, example, and source—each adds value without redundancy. The first sentence front-loads the primary use case and output, making it easy to scan.

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 2-param tool with no output schema, the description explains the output structure and provides a concrete example. It is self-contained enough for an agent to understand what the tool returns and when to invoke it.

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 100% with parameter descriptions and an enum. The description's example (500-person healthcare) clarifies how industry and employee_count combine, adding semantic meaning beyond the schema by showing how the inputs map to a real-world scenario.

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?

Clearly states the tool returns spend bands, category ranges, and outlier flags scaled to employee count for a specific industry and headcount. The example with a 500-person healthcare org makes the purpose concrete and distinguishes it from sibling tools by emphasizing employee-count scaling.

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?

Explicitly says 'Use when sizing a technology budget for a specific industry and headcount, or identifying category spend outliers,' providing clear usage context. It doesn't name alternative tools or exclusions, but the use case is specific enough to guide selection.

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