Abstract a specific technology or solution into its upper-level design, then analyze how that design is implemented to support top-level decisions.
拆解上位设计：从具体技术/方案向上抽象，再分析它怎么实现。
例如：
subject="K8s RBAC" → 上位设计是“权限控制/访问控制”
subject="MCP" → 上位设计是“模型上下文协议 / Agent 工具调用架构”
subject="树莓派摄像头识别" → 上位设计是“边缘 AI 系统”
| Name | Required | Description | Default |
|---|---|---|---|
| idea | No | ||
| subject | Yes |
Changes observed during successful MCP inspections.
v0.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the internal two-step process (abstract, then analyze implementation), which is meaningful behavioral context. However it says nothing about output shape, whether the analysis is stored/persisted, permissions, or side effects.
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 core definition is front-loaded in a single clause, followed by compact examples that earn their place by making an abstract concept tangible. Structure is clear and free of filler.
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?
For a 2-param analytical tool with no annotations and no output schema, the description conveys the transformation well but omits the 'idea' parameter entirely and gives no expectation of what the analysis returns, leaving a real gap.
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 coverage is 0%, so the description must compensate. The examples richly clarify what 'subject' means (K8s RBAC, MCP, Raspberry Pi camera) and what the resulting upper-level design is. The second parameter 'idea' is never mentioned, leaving half the inputs undocumented.
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 a specific transformation: abstract upward from a concrete technology/solution to a higher-level design, then analyze how it is implemented. The three worked examples make the intent concrete. It does not, however, distinguish this from siblings like reverse_reasoning or probe_technology_details.
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 examples imply where the tool applies, but there is no explicit when-to-use statement, no conditions or exclusions, and no named alternative among the numerous siblings (reverse_reasoning, probe_technology_details, list_design_options). The agent must infer routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.