Report solver errors, stuck states, or unexpected results to a local feedback log. Describe the problem and expected outcome to help improve the system without sending data externally.
AI 智能体在调用 solve 遇到卡点、错误、或认为结果有问题时，主动回报。回报内容仅落本地 feedback.log，不会外传。帮助作者持续改进。
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | 可选上下文：触发场景、输入特征等。 | |
| message | Yes | 反馈内容：遇到了什么、期望什么、实际得到什么。 |
Changes observed during successful MCP inspections.
v1.0.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the key behavior that content is only written locally to feedback.log and not transmitted externally, which is crucial for privacy expectations. It does not address return values or side effects beyond the log write, but the most important trait is covered.
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 compact and front-loads the trigger condition, then adds the local-only privacy note. Every sentence contributes usage context or a behavioral guarantee; the final sentence about helping the author improve is minor but not distracting.
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 simple feedback tool with two straightforward parameters and no output schema, the description covers trigger, destination, privacy, and purpose. It lacks explicit instructions on what to include in the message, but the schema already covers that, so it is complete enough for invocation.
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 100%, with both context and message having descriptions. The description adds little beyond the schema—it does not clarify formats or additional meaning, so baseline 3 is appropriate.
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?
Description clearly states the tool reports issues encountered when invoking solve, with specific trigger conditions and target (local feedback.log). This distinguishes it from the sibling solve tool, which is the main execution tool.
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 explicitly specifies when to use: upon blockers, errors, or suspect results while calling solve. It implies this is a reactive reporting mechanism rather than for routine use, though it does not explicitly name alternatives or exclusions beyond solve.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.