枚举感受词或视觉风格
word_list列出全部感受词(带共享度)或视觉风格(带定义与词集规模)。调 analyze 之前先拉一次可避免名字拼错。
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | 枚举哪一类 | words |
word_list列出全部感受词(带共享度)或视觉风格(带定义与词集规模)。调 analyze 之前先拉一次可避免名字拼错。
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | 枚举哪一类 | words |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already mark this as a safe read (readOnlyHint=true) on a closed domain (openWorldHint=false), so the description adds value by disclosing what each entry contains: a sharedness value for words and a definition plus vocabulary size for styles. It says nothing about ordering, pagination, or how large the result could be, which keeps it from reaching a 5.
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?
Two sentences with no filler: the enumeration content comes first, and the when-to-call-the-alternative note comes second. Every clause carries information the agent does not get from structured fields.
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 one-parameter read tool with no output schema, the description covers what is returned and why to call it, which is most of what is needed. The only gap is the lack of any indication of result size or ordering, which matters for a 'list all' tool.
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% and the single enum parameter is already documented as 'which category to enumerate', so the schema does most of the work. The description implies the value-to-output mapping (words→sharedness, styles→definitions and size), which helps, but it adds nothing about the default or behavior of an empty selection, so the baseline 3 applies.
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 title and description state a specific verb (list/enumerate) plus the two distinct resources (feeling words with sharedness, visual styles with definitions and vocabulary size), and the description names what extra data each enumeration returns. An agent can distinguish this bulk-enumeration tool from its siblings word_lookup, word_analyze, and style_get without opening any 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?
It gives an explicit timing rule ('pull once before calling analyze to avoid misspelling names'), which is exactly the kind of when-to-use guidance an agent needs. It does not, however, say when to use this versus word_lookup or style_get, so the routing guidance is incomplete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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