Add lab results

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

Annotations only cover the safety profile (write, non-destructive, non-idempotent, closed-world); the description adds atomicity ('writes all rows or none'), normalization behavior (catalogued tests stored in SI with bounds), and failure semantics (unknown spelling refused while naming accepted names, invalid cells/µL rejected). It also warns against the substitute write path. This is genuinely beyond what the structured fields convey.

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?

Four dense sentences, front-loaded with scope and limits, then behavioral rules, then the usage gate. No filler sentences and nothing repeated from the schema or annotations.

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?

Covers what to send, limits, validation outcomes, atomicity, and when to invoke; an output schema exists so return values need no explanation. One gap: with idempotentHint=false, a retried identical call would duplicate rows, and the description does not warn about that.

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?

The single top-level `values` parameter carries no schema description, so the description must supply shape constraints; it does so with the 50-row cap and the 'as printed, never convert' rule for number and unit. The remaining field semantics (metricName, recordedAt, reference bounds) live in the nested schema, but the description still adds the interpretation rule that values are transcribed verbatim while storage converts.

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?

States a specific verb (Append) and a precise resource (blood tests that are not core metrics), explicitly carving out the core-metrics territory handled by the sibling add_measurement. An agent can distinguish this from add_measurement and correct_value without opening any schema.

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?

Gives an explicit trigger ('Only when the user asked to add a value') and an explicit misuse case ('a failed correction is never turned into an add'), which routes the agent away from correct_value. It does not name add_measurement outright, relying on 'not core metrics' to imply the alternative.

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