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

The description adds behavioral context beyond annotations: 'bounded sample' indicates a limit on results, 'authoritative matching-row count' implies a count is returned, and the clarification that APC payments are aggregates not revenue/margin. Annotations already declare readOnlyHint=true, destructiveHint=false, and the description aligns without contradiction.

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 two sentences with no redundancy. The first sentence delivers the core purpose, and the second adds essential context. Every word adds value; the structure is front-loaded and efficient.

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?

The description covers the overall purpose and key filters but lacks details on parameter usage (e.g., default values, ranges, offset behavior) despite having an output schema. It is sufficient for tool selection but not for full invocation understanding. Given the complexity and available annotations, it is moderately 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?

With 0% schema description coverage, the description must compensate. It explains 'apc_code' and 'year' (exact), 'state' (optional filter), and 'limit' indirectly via 'bounded sample', but does not describe 'offset' or provide detailed parameter formats. The example in the schema helps, but the description alone lacks full parameter semantics.

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 uses a specific verb ('Return'), a precise resource ('bounded sample of hospital-level Medicare fee-for-service outpatient rows'), and explicit filters ('exact APC and year', 'optionally filtered by state'). It also adds a clarifying sentence distinguishing APC payments from revenue/margin, helping differentiate from sibling tools like medicare_inpatient_drg_market.

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 implies usage for outpatient APC data queries but does not provide explicit guidance on when to use this tool over alternatives (e.g., medicare_hospital_service_trend). No exclusions or when-not conditions are stated, only implicit context based on the tool's focus.

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