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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context: the result is a 'bounded sample' (not full dataset), includes 'authoritative matching-row count', and warns that 'Average payments are not hospital revenue or margin.' This goes beyond 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 consists of two concise sentences. The first sentence clearly defines the tool's function with no wasted words. The second sentence provides a critical caveat. Every sentence earns its place.

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 existence of an output schema, the description adequately covers the input and behavior. It explains the type of data (hospital-level, fee-for-service), the key filters, and the nature of the output (bounded sample with row count). It could mention data availability limitations (e.g., years) but is largely 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 description coverage is 0%, so the description must compensate. It mentions 'exact MS-DRG and year, optionally filtered by state', which covers drg_code, year, and state. However, limit and offset are only implied by 'bounded sample' and the example in the schema. The description adds meaningful context (exact match, optional filters, bounded sample) but could more explicitly describe all five parameters.

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 verb 'return', the resource 'hospital-level Medicare fee-for-service inpatient rows', and the constraints ('exact MS-DRG and year', 'bounded sample', 'optionally filtered by state', 'authoritative matching-row count'). This is highly specific and distinguishes it from sibling tools like medicare_hospital_service_trend or medicare_outpatient_apc_market, even without explicit contrasts.

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 contextual guidance: use this tool for exact MS-DRG and year, optionally by state, and notes it returns a bounded sample. It does not include explicit when-not-to-use or alternative tool names, but the context is sufficiently clear for an agent to determine applicability.

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