analyze_complexity

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

Annotations provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, indicating a safe, non-destructive, repeatable operation with closed-world behavior. The description adds no behavioral traits beyond this, but since annotations are comprehensive, the bar is lower. There's no contradiction with annotations, and the description doesn't mislead, so it earns a baseline score for not detracting from the structured data.

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 brief but inefficiently structured with redundant terms ('복잡도|복잡한지|complexity|how complex|난이도'), which adds noise without clarity. It's front-loaded but could be more concise by eliminating repetition. The single sentence earns some points for brevity but loses for wastefulness.

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 moderate complexity (2 parameters, 1 required), rich annotations (covering safety and behavior), and no output schema, the description is minimally complete. It states the purpose but lacks details on output format or usage context. With annotations handling behavioral aspects, it's adequate but could better address gaps like result interpretation.

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 clear descriptions for both parameters ('code' and 'metrics' with enum values). The description adds no meaning beyond the schema, as it doesn't explain parameter usage or semantics. With high schema coverage, the baseline is 3, reflecting adequate but no extra value from the description.

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 states the tool analyzes code complexity, which is a clear purpose, but it's somewhat vague with repetitive phrasing ('복잡도|복잡한지|complexity|how complex|난이도'). It doesn't explicitly differentiate from sibling tools like 'analyze_problem' or 'validate_code_quality', though the title 'Analyze Complexity' helps. The verb 'analyze' is specific, but the resource 'code complexity' could be more precise.

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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention sibling tools like 'analyze_problem' or 'validate_code_quality', nor does it specify contexts or exclusions for its use. Usage is implied by the name and description but not explicitly stated.

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