Queries Wuthering Waves echo artifacts by Chinese name and returns deduplicated Markdown data for LLM consumption.
获取库街区上的声骸详细信息并以 Markdown 格式返回。
Args:
artifact_name: 要查询的声骸套装的中文名称。
Returns:
包含声骸信息的 Markdown 字符串，
或者在找不到声骸或获取数据失败时返回错误消息。
| Name | Required | Description | Default |
|---|---|---|---|
| artifact_name | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v2.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns Markdown-formatted data or error messages on failure, which is useful behavioral context. However, it doesn't mention other traits like rate limits, authentication needs, data freshness, or whether it's a read-only operation (though implied by 'get'). For a tool with no annotations, this is adequate but lacks depth.
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 appropriately sized and front-loaded: the first sentence states the core purpose and output format. The Args and Returns sections are structured clearly without unnecessary details. Every sentence earns its place by providing essential information, making it efficient and easy to parse.
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?
Given the tool's low complexity (1 parameter, no nested objects) and the presence of an output schema (which handles return values), the description is mostly complete. It covers the purpose, parameter semantics, and output behavior. However, it lacks usage guidelines and deeper behavioral context (e.g., error conditions beyond 'not found' or 'failure'), which slightly reduces completeness for a tool with no annotations.
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?
The schema description coverage is 0%, so the description must compensate. It adds meaning by specifying that 'artifact_name' is the Chinese name of the artifact set to query, which clarifies the parameter's purpose beyond the schema's generic 'Artifact Name' title. Since there's only one parameter, this is sufficient to earn a high score, though it doesn't detail format constraints or examples.
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 clearly states the tool's purpose: '获取库街区上的声骸详细信息' (get detailed information about artifacts from the library district). It specifies the verb (获取/get) and resource (声骸详细信息/artifact details), and mentions the output format (Markdown). However, it doesn't explicitly differentiate from sibling tools like get_character_info, which likely retrieves character information instead of artifacts.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools (get_character_info, get_character_profile) or clarify scenarios where this tool is appropriate versus others. The only implied usage is when artifact details are needed, but no explicit context or exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.