Apply Data Quality Fix

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

Annotations indicate the tool is not read-only and not destructive. The description adds that it applies fixes and creates a Provenance record, plus authorization requirements. This adds useful behavioral context beyond the annotations, though it could detail consequences (e.g., whether the original resource is versioned).

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?

Two sentences and a header line concisely convey purpose, requirements, and core functionality. No filler; every sentence adds value.

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 explains what the tool does and its prerequisites, but it does not mention the return value or outcome (e.g., updated resource, success status). Given no output schema, this is a gap. However, the level of detail is adequate for a mutation tool with good parameter documentation.

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 100%, so all parameters are documented. The tool description briefly restates the 'fixes' array structure and patient_intent purpose, adding minimal new meaning beyond the schema. Baseline 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 applies patient-approved data quality fixes to FHIR resources and creates a Provenance record. It uses specific verbs and resources, but does not explicitly differentiate from sibling tools like curatr_evaluate or fhir_commit_write, though the patient-approval context provides some distinction.

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 mentions prerequisites: step-up authorization and human confirmation headers for clinical resources. This provides clear context on when the tool is appropriate, though it does not explicitly state when not to use it or list alternatives.

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