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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds substantial behavioral transparency beyond that: it states the data source, the return payload (USD price, unit, observation period), the cost ($0.02 atomic), and the post-quantum signed settlement receipt with a verification URL. This gives a complete picture of what the tool does and its side effects, with no contradiction to 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 is compact, consisting of three sentences. The first sentence states the core function, the second lists return values and use cases, and the third covers source, cost, and attestation. Each sentence carries relevant information for an agent, with no filler. It is slightly dense but well-structured and front-loaded with the primary 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 simple read-only tool with one parameter, annotations, and no output schema, the description is nearly complete. It covers what the tool does, why to use it, what data it returns, the source, cost, and verification mechanism. It does not describe the exact response format or mention rate limits, but these are not essential given the simplicity and available annotations. Overall, it leaves little 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?

The schema has one required parameter with an enum for the five commodities. The description lists those same commodities, which matches the enum, but adds little beyond the schema. It does add context about what the return includes (USD price, unit, observation period), indirectly giving meaning to the parameter, but the parameter itself is fully specified by the schema. Since schema coverage is 0%, the description partially compensates but does not provide deep semantic additions.

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 that the tool retrieves spot agricultural commodity prices for five specific commodities (soybeans, wheat, corn, cotton, coffee) from FRED/IMF data. It uses a specific verb (retrieves) and resource (agricultural commodity price), and narrows the scope enough to distinguish it from the broader sibling tool get_commodity_benchmark.

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 provides clear usage context by listing concrete use cases (crop hedging, food cost modeling, trade exposure agents) and identifies the data source (FRED/IMF). It does not explicitly exclude alternatives or mention when not to use the tool, but the focused scope and use cases give enough guidance for an agent to decide when this tool is appropriate.

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