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

Annotations already mark the tool as read-only and non-destructive. The description adds valuable behavioral context by detailing the nature of the output (domain weights, national benchmarks, improvement priorities) and providing a concrete example with specific numbers and methodology source. This goes beyond the 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 concise and front-loaded with the use case. The example is detailed yet efficiently communicates typical output and improvement scenarios. Every sentence adds value without unnecessary fluff.

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 the lack of an output schema, the description does a good job explaining the high-level return content and provides illustrative data. However, it leaves parameter usage ambiguous and does not clarify whether inputs are required or how they affect results. For a tool with 3 optional parameters, this is a notable gap.

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% and the description does not explain the three parameters (state, hospital_name, current_star_rating). The example indirectly references current_star_rating ('hospitals moving 3 to 4 stars'), but state and hospital_name are entirely unexplained, leaving the agent without guidance on how to populate them.

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 purpose: 'Use when advising on CMS Hospital Star Rating strategy or benchmarking a hospital quality performance trajectory.' It specifies what it returns: 'domain weights, national distribution benchmarks, and improvement priorities.' This distinguishes it from siblings like get_hospital_care_compare_quality by focusing on strategy and benchmarks rather than general quality data.

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' guidance is provided, making the intended context clear. However, it does not mention when not to use this tool or suggest alternatives such as get_hospital_care_compare_quality, so it stops short of full exclusions.

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