validate_code_quality

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

Annotations provide clear behavioral hints (readOnlyHint: true, idempotentHint: true, destructiveHint: false), indicating a safe, non-mutating operation. The description adds no additional behavioral context (e.g., what quality standards are used, if results are cached, or performance implications), but it doesn't contradict the annotations. With annotations covering key safety aspects, the description's lack of extra detail is acceptable but not exemplary.

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 a string of keywords separated by pipes and dashes, lacking coherent structure or front-loaded clarity. It's not a proper sentence or paragraph, making it inefficient for quick comprehension. While concise in length, it fails to communicate effectively, as the keyword jumble requires parsing rather than delivering immediate understanding.

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 the tool's complexity (4 parameters, no output schema) and rich annotations, the description is insufficient. It doesn't explain what the tool returns (e.g., a quality score, issues list), how validation is performed, or tie parameters to outcomes. With annotations handling safety but no output schema, the description should provide more context about results and behavior to be complete for a quality analysis tool.

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%, with all parameters well-documented in the schema (e.g., 'code' as 'Code to validate', 'type' with enum values). The description adds no parameter-specific information beyond what the schema provides, such as explaining how 'strict' affects validation or what 'metrics' entail. Given the high schema coverage, a baseline score of 3 is appropriate, as the description doesn't enhance parameter understanding.

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 provides a list of keywords ('품질|리뷰|검사|quality|review code|check quality|validate|코드 리뷰') that suggest the tool validates code quality, but it lacks a clear, specific statement of purpose. It doesn't explicitly state what the tool does (e.g., 'Analyze code against quality metrics') or distinguish it from siblings like 'analyze_complexity' or 'check_coupling_cohesion'. The keyword approach is vague rather than definitive.

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 offers no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'analyze_complexity' (for complexity analysis) or 'check_coupling_cohesion' (for coupling/cohesion checks), nor does it specify contexts where this tool is preferred (e.g., for comprehensive quality validation vs. specific analyses). Without such guidance, users must infer usage from the tool name and parameters alone.

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