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

With no annotations, the description carries the full transparency burden. It discloses important behavioral context: the database contains many torque/speed variants (19 torque calibers, 37 speed calibers, even Gbps and rad/s mixed in), and the tool returns normalization aids rather than modifying data. It does not state edge cases like failure modes, but it goes well beyond a bare 'return parameter semantics' statement.

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 two sentences, front-loaded with the core purpose, and every clause earns its place: the first sentence establishes the problem (semantic ambiguity), and the second lists the outputs and use case. There is no filler.

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 zero-parameter metadata lookup tool, the description is complete: it explains why the tool exists, the data-quality issues it addresses, what it returns, and how to apply the result. Since there is no output schema, the detailed enumeration of return components (physical red lines, unit conversions, comparability grades, vendor inquiry list) is sufficient.

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 has zero parameters and the input schema is empty, so the baseline is 4. The description adds context about the domain (torque/speed calibers and unit ambiguity) that helps an agent understand what the tool operates on, even though there are no explicit parameters to document.

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 opens with a specific action ('获取参数口径规范') and clearly identifies the resource (parameter definition norms). It then lists concrete outputs (physical red lines, unit conversion, comparability grading, vendor inquiry checklist) and states the intended use ('判断两份参数表到底能不能直接比较'), which distinguishes it from sibling tools like search_components or recommend_for_application.

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: when determining whether two parameter tables can be directly compared, especially in cross-vendor contexts with ambiguous units. It does not explicitly name sibling tools or state when not to use it, but the context is clear enough for an agent to select it appropriately.

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