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 the BLS OES 2024 data source and return metric, but does not disclose behavior for omitted state or other edge cases. 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 is a single dense paragraph with the use case and return type front-loaded. The example adds useful context but increases length. All sentences are relevant, though the retention-risk insight could have been omitted without losing core meaning.

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 two parameters and no output schema, the description covers purpose, return metric, data source, and gives a concrete example. Gaps remain around parameter format and state-omission behavior, but the example partially compensates. The low complexity and available annotations make this reasonably complete.

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 0%, so the description must compensate. It names 'specialty and state' as the filters, and the example provides sample values. However, it does not clarify accepted formats (e.g., full state names vs. codes), that state is optional, or how specialty titles are normalized.

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 it 'Returns median total compensation by specialty and state from BLS OES 2024 data' and provides explicit use cases for employment agreements, recruitment, and compensation reports. The example with Illinois cardiologist illustrates the specific metric, distinguishing it from sibling 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?

Explicit 'Use when' clause names three scenarios, giving clear context for when to invoke this tool. However, it does not mention when not to use it or directly name alternative tools, so it stops short of fully differentiating from siblings.

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