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

The annotations already mark the operation as read-only and non-destructive; the description adds meaningful context beyond that by disclosing the exact returned metrics, the $0.02 USDC per-call cost, cryptographic attestation, and a verification URL. No contradiction with annotations.

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 front-loads the use case and outputs before adding an illustrative example, attestation, and pricing. It is somewhat long, but each sentence carries selection- or invocation-relevant information, so it remains efficient.

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 benchmark lookup with no output schema, the description covers purpose, filters, returned metrics, cost, and a realistic example. The main omissions are parameter formats and explicit optionality, but the schema covers requiredness and the example mitigates format ambiguity.

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 50%, and the description partially compensates by naming pay_item_code as the central code and listing owner, state, and county as filters, plus a concrete example. However, it does not specify formats for pay_item_code or county beyond the example, so some parameter detail remains under-specified.

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 action—benchmarking a bid line-item price against recent public lettings—and specifies the returned stats (min, max, average, sample count) for a pay item code with optional filters. This clearly distinguishes it from data-lookup siblings like get_intel_lettings_search or generic benchmark tools.

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?

It opens with an explicit 'Use when...' condition targeting pre-estimate bid benchmarking, which gives clear selection context. It does not name alternatives or state when not to use it, so it stops short of full exclusionary guidance.

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