Observation Statistics

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds that it is a standard FHIR operation and only supports valueQuantity, which is behavioral context beyond annotations. No contradictions.

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 with three sentences. The first sentence immediately states the tool's purpose, and no extraneous information is included. Efficiently structured.

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 simplicity and no output schema, the description covers the key aspects: purpose, supported value type, and filtering. It does not detail the output format, but the listed statistics (count, min, max, mean) provide reasonable expectation.

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% and both parameters are described in the schema. The description adds meaning by explaining filtering and providing an example format (LOINC code) for the 'code' parameter, which goes beyond the schema.

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 computes statistics (count, min, max, mean) over numeric Observation values using valueQuantity, and mentions FHIR R4 standard. This is specific and distinguishes it from sibling tools like fhir_search or fhir_interpret_labs.

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 context on filtering by patient and/or code, and specifies that only valueQuantity is supported. It implies usage for numeric observation statistics but does not explicitly exclude alternative scenarios or mention when not to use the tool.

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