Work backward from a given solution or subject to uncover the problem it solves, its reason for existing, and whether you need it.
反向梳理：从具体方案反推它解决什么问题、为什么存在、我们需要吗。
例如：
subject="K8s RBAC" → 反推：多用户/多资源环境下需要访问控制
subject="MCP" → 反推：Agent 需要标准化发现和调用工具
subject="RAG" → 反推：大模型需要利用私有/最新知识
| 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?
No annotations are provided, so the description carries the full behavioral burden. It does convey the nature of the reasoning output (the problem solved, why it exists, whether it's needed), which is genuine behavioral context, but says nothing about determinism, latency, or how the optional 'idea' input changes behavior.
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 operation is stated up front in one line, and the examples are compact and directly useful for understanding invocation. Structure is efficient with little waste.
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?
With no output schema and no annotations, the description is the sole source of context. It adequately explains the reasoning operation but omits any explanation of the 'idea' parameter and offers no guidance on outputs or when this analysis should be chosen over sibling tools.
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 0%, so the description must compensate. It illustrates the required 'subject' parameter with examples but never mentions the optional 'idea' parameter at all, leaving half of the inputs semantically 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 names a specific cognitive operation (reverse-inferring the problem/solution-rationale behind a given subject) and illustrates it with three concrete examples, so an agent understands what the tool produces. It does not, however, distinguish itself from potentially overlapping siblings such as probe_technology_details or probe_idea_question.
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?
Usage is implied through examples of valid subjects, so an agent can infer how to invoke it. But there is no explicit statement of when to choose this over probe_technology_details or list_design_options, and no when-not guidance or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.