list_lab_results

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond those annotations: the tool returns IDs, defaults start_date to 365 days ago, end_date to today, and clarifies that update/delete can resolve results independently. It does not describe every output detail, but an output schema exists, so this is not a major gap.

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 well-structured and front-loaded with the core purpose. Each subsequent sentence adds distinct value: sibling relationship, use cases, filtering, and inference defaults. There is no filler or repetition of schema fields, and the formatting makes the INFER instructions easy to parse.

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 low complexity (0 required parameters, 4 optional well-documented parameters, no nested objects, and an output schema), the description covers all important context. It explains when the tool is needed, when it is not needed, what IDs are for, and how defaults should be inferred. Nothing essential is missing for correct selection and invocation.

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%, with each parameter already documented with format, defaults, and optionality. The description adds value by instructing the agent to 'INFER — do not ask' the defaults and by explaining why start_date defaults to 365 days ago ('labs are infrequent'). This goes beyond the schema and gives practical guidance for parameter selection.

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 states a specific verb and resource: 'List lab/biomarker results within a date range, including each result's ID.' It clearly distinguishes itself from related siblings like list_lab_markers, update_lab_result, and delete_lab_result by focusing on historical review and ID retrieval. No ambiguity remains about what the tool does.

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 explicitly states when to use the tool: to review lab history, answer trend questions, or get an ID after ambiguous update/delete matches. It also explicitly says this tool is 'no longer required' before update_lab_result or delete_lab_result, giving clear exclusion criteria. The INFER defaults further guide autonomous invocation without asking the user.

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