Translate an error message into why it happened and what to do next, using rule-based matching with millisecond returns and no LLM calls. Diagnose any error text manually.
把一条报错文本翻译成"为什么 + 怎么办"。 任何工具返回错误时都会自动附带诊断；这个工具用于手动诊断任意一段报错。 规则匹配、毫秒级返回，不调 LLM。
| Name | Required | Description | Default |
|---|---|---|---|
| error | Yes | 报错原文 | |
| output | No | 命令输出（可选） | |
| command | No | 导致它的命令（可选，用于判断能否自动修补） |
Changes observed during successful MCP inspections.
v0.2.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and does disclose key traits: rule-based matching, millisecond latency, and no LLM invocation — so the agent knows it is cheap, deterministic, and safe to call freely. What is missing is any hint of the response shape or whether a low-confidence/unmatched input returns nothing useful.
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?
Three short sentences, front-loaded with the value proposition, then the relation to automatic diagnostics, then the cost profile. No filler or repetition of the tool name.
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 low-risk, three-parameter analysis tool with no output schema, the description covers purpose, cost, and invocation context adequately. It could say more about what the 'why + what to do' payload looks like or when a match fails, but nothing essential is missing to call it correctly.
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%, so all three parameters are already documented in the schema. The description only refers generically to 'an arbitrary error text' and adds nothing about how `output` or `command` change the diagnostic (the schema's note about auto-patching is the only guidance). 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?
States a concrete transformation: turn an error string into 'why + what to do'. It also positions the tool against the built-in automatic diagnosis that accompanies tool errors, so an agent can tell what this adds. The verb and resource are both specific, not a restatement of the name.
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?
Explicitly says the tool is for manually diagnosing an arbitrary error text, and notes that errors from any tool already carry a diagnosis — a strong implicit 'when you don't need this'. It stops short of an explicit exclusion statement (e.g., 'do not call this on an already-diagnosed tool error').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.