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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is low; the description still adds real semantic value by warning that vocabulary presence does not imply a firm has that capability, preventing a common over-inference. It does not describe the shape of the returned taxonomy, but the output schema covers that.

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?

Three short sentences, front-loaded with what is read, then the caveat, then the operational rule. No filler or restatement of the name.

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?

With zero params, full annotation coverage and an output schema, the definition needs only purpose plus caveats, both of which are present. A slightly stronger version would state the call ordering relative to search_cabinets explicitly.

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 tool takes zero parameters, so there is nothing to disambiguate and the baseline is 4. The description correctly spends no space on parameter explanation.

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 (read) and a specific resource (the exact sector, service, software and language values accepted by search_cabinets), which also pins down the relationship to the sibling that consumes them. An agent can tell immediately this is a vocabulary lookup rather than a 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?

It establishes the context of use by tying the values to search_cabinets and adds a concrete rule ('never replace an unknown requested criterion with a broader category without user agreement'). It stops short of explicitly saying 'call this before search_cabinets to validate parameters', so the sequencing is left slightly implicit.

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