Interpret Lab Results

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

Annotations (readOnlyHint=true, destructiveHint=false) already indicate a safe read operation. The description adds context about decision support not being diagnosis, but does not disclose further behavioral traits such as error handling, authentication needs, or rate limits.

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 extremely concise, delivering the core purpose and caveat in just two sentences. It is front-loaded and contains no redundant or filler content.

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?

While the description explains output (flags and summaries) and the read-tier nature, it does not address what happens when no parameters are provided or describe the response format in detail. Given no output schema and optional parameters, more context would be beneficial, but the description is adequate for common use cases.

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 descriptions for all three parameters. The tool description does not add significant meaning beyond the schema; it only implies that the tool can be called with a single observation or a bundle. 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 interprets lab Observations against reference ranges, flags values, and returns summaries. It uses a specific verb ('Interpret') and resource ('lab Observations'), and distinguishes itself from sibling tools such as fhir_read or fhir_search.

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 includes 'Decision support, not diagnosis. Read-tier,' which provides context on when to use the tool (decision support) and its read-only nature. However, it does not explicitly mention when not to use it or directly compare it to alternative tools for similar tasks.

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