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

With no annotations provided, the description carries the full burden and does a good job: it explicitly states conclusions are rule-based on manufacturer public declarations, not lab-tested, and that undeclared dimensions are marked as 'cannot determine' and not counted as compatible/incompatible. This is valuable behavioral context for an agent interpreting results, though it does not cover authentication or side effects, which are likely less relevant here.

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 concise sentences: the first states the main purpose and return types, the second delivers an important caveat about inference basis and undeclared dimensions. It is front-loaded, every sentence adds value, and there is no redundant or vague wording.

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?

Given a 2-parameter tool with no output schema, the description sufficiently explains the core behavior: what it returns (per-dimension, overall, confidence) and the inference logic. It lacks a concrete output format example, but the stated return categories and the detailed parameter schema make the tool actionable. A fully exhaustive spec would be excessive for this simplicity.

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 100%, so the baseline is 3. The description itself does not add parameter-specific semantics beyond the schema, which already thoroughly explains ID format, retrieval via search_components, and the ID-prefix mismatch warning. The description's mention of the four dimensions is context for the tool's logic, not parameter syntax, so it does not elevate the score.

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 uses the specific verb '判定' (determine) and clearly identifies the resource: compatibility of two components across four stated dimensions (protocol/electrical/mechanical/software). It also lists what the tool returns (per-dimension conclusions, overall judgment, confidence), distinguishing it from sibling tools like get_component_detail 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 implies the tool is for cross-component compatibility checking and even notes in the parameter schema that cross-category comparison is this tool's purpose. However, it does not explicitly state when to use this tool versus alternatives or provide exclusion criteria, though the context is clear enough for an agent to infer appropriate use.

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