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

Annotations already indicate read-only, idempotent, open-world, and non-destructive behavior. The description adds valuable context beyond that: cost ('Free, always', 'it is cheap'), data privacy ('returns no customer, order, or payment identifier because the store never sends any'), and data freshness reporting. No contradiction with annotations.

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 front-loaded with the core purpose, then adds cost, a compact return-item list, usage ordering, and a privacy note. Each sentence contributes distinct information with no filler or redundancy.

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-parameter read-only health snapshot with an output schema, the description covers everything an agent needs: what it does, what it returns, when to call it, cost, and privacy behavior. The optional parameter is fully documented in the schema.

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 only parameter, window_seconds, is fully described in the schema with default, minimum, and maximum. The tool description itself does not add parameter-level meaning beyond the word 'window', so the 100% schema coverage establishes the baseline.

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 explicitly states the tool's purpose: to assess whether anything is wrong with the store's checkout and payment path and whether a full diagnosis is worth running. It also lists the concrete signals returned, which distinguishes it from sibling tools like diagnose_revenue_incident by positioning it as a cheap triage snapshot.

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 gives direct usage guidance: 'Call this FIRST' and explains that diagnosis_recommended tells whether the expensive call would have anything to work with. This explicitly tells the agent when to use this tool relative to the heavier diagnosis alternative.

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