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

Annotations already indicate a safe read-only operation, so the bar for additional transparency is lower. The description adds value by specifying the data returned (market rates for categories and facility types), providing a sample output/interpretation threshold, and citing data sources. It does not mention response structure or pagination, but these are less critical for a benchmark lookup tool.

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 four sentences: use case, return value, example, and source. Every sentence provides useful information, and the example is concrete and instructive. It is slightly longer than needed, but the structure is logical 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 two-parameter read-only tool with no output schema, the description covers the essential aspects: when to use, what data is returned, an illustrative example, and data provenance. It could benefit from explicit parameter mapping, but overall it is complete enough for an agent to invoke correctly.

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 0%, so the description must compensate. It lists valid category examples (EHR, staffing, food service, waste management, med-surg), which effectively documents the 'category' parameter. However, it does not explain the 'vendor_name' parameter beyond implying it from the tool name and use case, nor does it clarify parameter formats or required/optional status. Partial compensation but not complete.

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 function: it returns market rates for specific vendor categories (EHR, staffing, food service, etc.) by facility type. It also provides a precise use case ('benchmarking a healthcare vendor quote') and includes a concrete example, making it unmistakable what the tool does.

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: 'Use when benchmarking a healthcare vendor quote or preparing a supply chain contract negotiation.' It does not explicitly name alternatives or exclude scenarios, but the context is clear enough that a model can infer when to choose this tool over siblings.

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