get_analysis_json

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description states it returns '原始 JSON 数据结构' (raw JSON data structure), which implies a read-only operation that doesn't modify data. However, it doesn't disclose other behavioral traits like error conditions, rate limits, authentication requirements, or what the JSON structure contains. The description adds some context about the return format but lacks comprehensive behavioral details.

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 extremely concise and well-structured with just two sentences. The first sentence states the core purpose, and the second sentence provides differentiation from the sibling tool and usage context. Every sentence earns its place with no wasted words.

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 simple nature (single parameter, no output schema, no annotations), the description is reasonably complete. It clearly explains what the tool does, how it differs from its sibling, and when to use it. However, without an output schema, the description could better explain what the returned JSON structure contains. The lack of annotations means the description should ideally cover more behavioral aspects.

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 description coverage is 100% with one parameter ('problem_text') fully documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter information in 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 clearly states the tool's purpose: '获取决策分析的原始 JSON 数据结构' (get the raw JSON data structure for decision analysis). It specifies the verb ('获取' - get) and resource ('决策分析的原始 JSON 数据结构' - raw JSON data structure for decision analysis), and explicitly distinguishes it from its sibling tool 'analyze_decision' by noting it returns structured JSON data instead of formatted text.

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 versus its alternative: '与 analyze_decision 功能相同，但返回结构化的 JSON 数据而非格式化文本。适用于需要程序化处理分析结果的场景。' (Same functionality as analyze_decision, but returns structured JSON data instead of formatted text. Suitable for scenarios requiring programmatic processing of analysis results). This clearly defines the use case and names the alternative tool.

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