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

With no annotations, the description carries the full burden and does reasonably well: it discloses the reference standard (130/80), the returned outputs (average, classification, critical-value flag, 722 method), and the input constraint (max 6 readings). It does not describe error handling for malformed input, which keeps it short of a 5.

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?

A single dense sentence, front-loaded with the framework and standard, followed by the return contract and input example. Little waste, though the packing of several facts into one clause-chain slightly reduces scannability.

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?

For a single-input classification tool with no output schema, the description covers the input format/limit, the evaluative standard, and the shape of the result. That is sufficient for an agent to call it correctly; only edge-case behavior is left unspecified.

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 0%, so the description must compensate, and it does: it gives a concrete format example ("128/82,134/86") and a cardinality limit (最多 6 筆) that the bare string schema does not express. This meaningfully clarifies the single parameter.

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?

Specific verb+resource: "判讀居家血壓" (interpret home blood pressure), and it names the governing standard (Taiwan 2022 guideline, 130/80). This clearly distinguishes it from the sibling health__check_blood_glucose, which handles a different metric.

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 implies the use case (interpreting home BP readings against a guideline) but gives no explicit when-to-use, when-not-to-use, or alternative routing. Usage is inferred rather than stated.

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