Evaluate Data Quality

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

The description adds significant behavioral context beyond the annotations: it specifies external terminology services used (tx.fhir.org, NLM, RXNAV), output format (plain language with impact descriptions and suggestions), and confirms read-only with 'no step-up required.' No contradiction with readOnlyHint=true.

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 three concise sentences, front-loaded with the core purpose, and each sentence adds distinct value: purpose, technical detail, and behavioral trait. No unnecessary words.

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 tool's complexity (evaluating FHIR data quality with multiple services), the description covers the key aspects: what it does, how it does it, what output looks like, and its read-only nature. Even without an output schema, the output description is sufficient. Annotations cover safety profile. The description feels complete for an agent to use 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 coverage is 100% with clear descriptions for both parameters. The description does not add new information about parameter semantics beyond what the schema already provides, thus baseline score of 3 is appropriate.

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 evaluates a FHIR resource for data quality issues, specifying it checks coding elements against public terminology services and structural rules. This distinguishes it from sibling tools like fhir_validate or guardrail_conformance, which are more about validation or conformance.

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 data quality evaluation of FHIR resources, but does not explicitly state when to use this tool over alternatives like fhir_validate or curatr_apply_fix. It provides helpful context about the checks performed, but lacks direct guidance on exclusions or when not to use it.

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