Updates a Chinese idiom's mastery status based on the learner's actual performance in conversation, letting the AI record whether it is learning, reviewing, or mastered.
根据对话中用户的实际表现，更新成语的掌握状态。由 AI 自行判断调用，服务端不做判分。
Args: word: 成语原文 status: learning=还不熟/答错/卡壳；reviewing=大致理解但不够稳；mastered=能准确解释并举一反三
| Name | Required | Description | Default |
|---|---|---|---|
| word | Yes | ||
| status | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation readOnlyHint=false already marks this as a write. The description adds meaningful context: judging is delegated to the model and the server does not score, which sets expectations for the write. However, it does not state whether an existing record is overwritten, what happens if the idiom is unknown, or whether the update is one-way.
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 main sentence comes first and is followed by the Args block; every line adds information that is used at call time. The status definitions are dense but justified, since they replace a missing enum.
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?
An output schema exists, so return values need not be described, and the annotation covers write semantics. Even so, for a mutation tool with 0% schema coverage, the definition omits whether the 'word' must already exist, and whether repeated calls update or append. These gaps are minor but real.
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 has to carry the parameters, and it does this well for 'status': three named values (learning/reviewing/mastered) with concrete decision criteria that are not encoded as a schema enum. By contrast, 'word' gets only '成语原文', with no detail on whether it must be the exact canonical form or must already exist.
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 verb and resource: '更新成语的掌握状态' (update an idiom's mastery status), scoped to the user's demonstrated performance in the conversation. This clearly distinguishes it from the siblings 'progress' (which reads) and 'reset_word' (which clears), so an agent can tell what this tool does without opening the schema.
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?
'由 AI 自行判断调用，服务端不做判分' explicitly tells the agent that it is the one deciding when to call, and that no server-side grading happens. That resolves the main ambiguity for a judgement-writing tool. It does not name the alternative actions (implicitly reading status via 'progress', or clearing status via 'reset_word'), so the routing is implied rather than spelled out.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.