analyze_decision

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 does an excellent job describing what the tool does (5 specific functions), what it doesn't do (won't provide recommendations), and its philosophical approach ('expose reality structure'). However, it doesn't mention technical behaviors like response format, processing time, or error conditions that would be helpful for an AI agent.

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 perfectly structured and front-loaded with the core purpose, followed by specific functions in a numbered list, and concluding with important limitations. Every sentence earns its place by adding distinct value, with no redundancy or wasted words. The bilingual presentation (Chinese with English translation) is efficiently handled.

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 (5 distinct functions) and absence of both annotations and output schema, the description does an excellent job explaining what the tool does and doesn't do. However, without an output schema, the description doesn't specify what format the analysis results will be returned in, which is a gap for an AI agent trying to use this tool effectively.

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 schema has 100% description coverage for its single parameter, so the baseline is 3. The description adds meaningful context by explaining that the problem_text parameter should contain 'emotional, positional, anxious natural expressions' that will be 'de-emotionalized and restructured.' This provides valuable semantic context beyond the schema's technical 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 explicitly states the tool's purpose as 'structured strategy analysis for real-world problems' and clearly distinguishes it from a suggestion generator. It provides a specific verb ('analyze') and resource ('decision problems') while differentiating from the sibling tool get_analysis_json by emphasizing its unique 'expose reality structure' approach rather than generating recommendations.

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 explicit guidance on when to use this tool ('for structured strategy analysis of real-world problems') and when not to use it ('will not tell you whether to do something'). It clearly distinguishes this tool's purpose from alternatives by stating it's 'not a suggestion generator' and only helps 'see the structure and boundaries of decisions'.

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