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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond annotations: the cost per call ($0.05), the payment-required error for unpaid calls, and the exact output structure (p10, p25, median, p75, p90, mean, company count). It doesn't describe pagination or rate limits, but the output schema covers return values.

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 compact and front-loaded: it states the output structure first, then gives usage examples, then names alternatives, then discloses cost. Every sentence earns its place, and the cost disclosure is placed at the end where it doesn't distract from the core purpose.

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 read-only, idempotent benchmark tool with a rich output schema and 100% parameter documentation, the description is complete. It covers what the tool returns, when to use it, when not to use it (via sibling routing), and the cost/error behavior. Nothing an agent needs to call it correctly is missing.

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 all four parameters. The description adds a few examples of metric values (gross_margin, operating_margin, etc.) and clarifies the size bucket thresholds (micro under 10M USD, small 10M+, etc.) in the schema. However, the description itself doesn't add much beyond what the schema provides, so 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 states a specific verb ('benchmark'), a precise resource (percentiles for one financial metric per fiscal year, 2-digit SIC industry group, and revenue size bucket), and names the exact percentile values returned. It clearly distinguishes itself from siblings by naming compare_to_peers and market_insight as alternatives for different questions.

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 guidance with example questions ('what is the median gross margin of large software companies?') and explicitly names alternatives (compare_to_peers for placing one company in its cohort, market_insight for margin medians across every industry). It also discloses the cost and payment-required error behavior, which is critical usage context.

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