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

Even though annotations already declare readOnlyHint=true and idempotentHint=true, the description goes well beyond them. It discloses the privacy guarantee ('AGGREGATE ONLY — never a name, never a row'), the five-profile minimum floor with its insufficient_data fallback, the loose-matching behavior of skill, and the distinction between listed and paid rates. These are substantial behavioral traits that the annotations cannot convey, and none contradict the structured metadata.

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 longer than average but every sentence earns its place — definition, use cases, parameter behavior, return shape, privacy floor, and sibling differentiation are each covered once with no filler. The core definition is front-loaded before the usage guidance. Minor redundancy with the schema's mention of the five-profile floor and insufficient_data keeps it from a 5, but the added reasoning ('a statistic over fewer than that describes an individual') justifies the repetition.

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 that an output schema exists and annotations are rich, the description covers everything an agent needs to call this tool correctly: return shape (p25/median/p75/p90, Top Rated share, sample size), the insufficient_data edge case, privacy constraints, parameter matching semantics, and when to choose a different tool. There are no meaningful gaps for an aggregate read-only query tool.

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%, so the baseline is 3 and the schema already documents both parameters well. The description adds genuine value on top: concrete valid skill examples ('django', 'react native', 'smm'), reassurance that country must be spelled as it appears on a profile, and the behavioral consequence that narrowing by country can trigger insufficient_data. This exceeds pure schema repetition without being redundant.

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, precise definition: 'What freelancers with a given skill LIST as their hourly rate, as a distribution rather than an average.' This names the resource (listed freelancer rates), the operation (benchmarking), and the distinguishing trait (distribution vs. average). It also explicitly differentiates from get_buyer_quality, ensuring the agent can separate it from its closest sibling without inspecting either schema.

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 context: 'Use this to sanity-check a rate before quoting, or to see whether a posted budget is above or below what the market asks.' It then names the alternative tool and the condition that selects it: 'It is not what buyers paid — for that, ask get_buyer_quality about a specific client.' Both the trigger conditions and the exclusion are spelled out, leaving nothing to inference.

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