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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the read-only nature is known. The description adds behavioral context beyond that: it specifies the return content (spend bands, category ranges, outlier flags), includes a concrete example with thresholds, discloses the data source (Stratalize workforce-scaled composite), and notes the cost ($0.02 USDC per call). This is valuable extra transparency, though it doesn't cover error handling or rate limits.

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 a single paragraph that follows a logical structure: usage context, output summary, illustrative example, data source, and cost. It is information-dense without being verbose. The example is concise and directly demonstrates the tool's value. No wasted words, though it could be slightly more compact.

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 tool is a simple read-only query with two well-described parameters and no output schema, the description covers the essentials: purpose, inputs, output type, example, and cost. It doesn't explicitly state the output format or error conditions, but for a budget-sizing tool with this simplicity, it's adequate. The example provides a clear expectation of the result.

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% for both parameters, so the schema already describes industry and employee_count. The description enhances this by explaining how the parameters interact: employee_count scales the spend bands, and industry selects the vertical. The example (500-person healthcare org) illustrates the semantic meaning of both parameters, adding value beyond the schema's terse descriptions.

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: sizing technology budgets by industry and headcount, and identifying category spend outliers. It specifies the resource (spend profile), the inputs (industry and employee count), and the outputs (spend bands, category ranges, outlier flags). This differentiates it from siblings like get_industry_spend_benchmark by emphasizing employee-count scaling and outlier detection.

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 opens with 'Use when...' giving explicit context for when to apply the tool. It covers two primary use cases: budget sizing and outlier identification. It doesn't explicitly name alternative tools or exclusion criteria, but the guidance is clear and sufficient for an agent to decide. Given the large sibling list, naming alternatives would improve it, but it's not required.

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