analyze_requirement_trap

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool does (analyzes requirements, detects issues, generates questions) and the output format (JSON with interview need and question list), which is helpful. However, it lacks details on error handling, performance characteristics, or any constraints like rate limits or authentication needs that would be important for an analysis tool.

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 well-structured and concise. It starts with the core purpose, details the analysis process, and clearly documents the single parameter and return format in separate sections. Every sentence adds value without redundancy, and it's appropriately sized for a tool with one parameter and an output schema.

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 (analysis with potential question generation), no annotations, and the presence of an output schema, the description is mostly complete. It explains the tool's function, parameter semantics, and output structure. The output schema likely details the JSON format, so the description doesn't need to elaborate on return values. However, it could benefit from more behavioral context like error cases or usage prerequisites.

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 description adds significant meaning beyond the input schema. The schema only indicates 'user_input' is a required string, but the description explains it's '用戶的系統需求描述' (user's system requirement description), clarifying the expected content. With 0% schema description coverage and only one parameter, this compensation is effective, though it could specify format or length expectations.

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 clearly states the tool's purpose: '分析用戶需求並檢測是否需要蘇格拉底面試' (analyze user requirements and detect if Socratic interviewing is needed). It specifies the verb (analyze/detect) and resource (user requirements), and distinguishes from siblings by focusing on requirement analysis rather than environment checking or project delivery. However, it doesn't explicitly differentiate from validation or code-checking siblings.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention when this analysis should be performed (e.g., before validation, after initial requirements gathering), nor does it reference any sibling tools like 'mmla_validate_code' or 'record_socratic_answers' that might be related. Usage is implied 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.