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get_artifact_info

Retrieve detailed artifact set information from Wuthering Waves in Markdown format by providing the Chinese name of the artifact set.

Instructions

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。
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
artifact_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool registration and primary handler for 'get_artifact_info'. This is the entry point for tool execution in FastMCP, which delegates to the artifact service.
    @mcp.tool()
    async def get_artifact_info(artifact_name: str) -> str:
        """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。
        """
        try:
            _, artifact_service = get_services()
            return await artifact_service.get_artifact_info(artifact_name)
        except (DataNotFoundException, ServiceException) as e:
            # These exceptions already have user-friendly messages
            return str(e)
        except Exception:
            return f"错误:处理 '{artifact_name}' 时发生意外错误。请检查服务器日志。"
  • Core business logic implementation of get_artifact_info in ArtifactService. Retrieves data from repository, parses content, generates Markdown, and handles errors.
    async def get_artifact_info(self, artifact_name: str) -> str:
        """Get comprehensive artifact information.
    
        Args:
            artifact_name: Name of the artifact set to query.
    
        Returns:
            Markdown formatted artifact information.
    
        Raises:
            ServiceException: If artifact retrieval fails.
        """
        try:
            self.logger.info(f"Getting artifact info for: {artifact_name}")
    
            # Get artifact raw data
            artifact_raw_data = await self._get_artifact_data(artifact_name)
    
            # Parse artifact content
            artifact_parsed_data = await asyncio.to_thread(
                self.content_parser.parse_artifact_content, artifact_raw_data
            )
    
            # Generate markdown
            artifact_markdown = self.markdown_service.generate_artifact_markdown(artifact_parsed_data)
    
            if not artifact_markdown.strip():
                self.logger.warning(f"Generated empty artifact info for: {artifact_name}")
                return f"成功获取 '{artifact_name}' 的声骸数据,但解析后的内容无法生成有效的 Markdown。"
    
            self.logger.info(f"Successfully generated artifact info for: {artifact_name}")
            return artifact_markdown
    
        except DataNotFoundException:
            error_msg = f"Artifact set '{artifact_name}' not found"
            self.logger.error(error_msg)
            return f"错误:未找到名为 '{artifact_name}' 的声骸套装。"
    
        except Exception as e:
            self.logger.error(f"Failed to get artifact info for {artifact_name}: {e}")
            raise ServiceException(f"Artifact info retrieval failed: {e}")
  • Type protocol/interface definition specifying the signature for get_artifact_info method.
    class ArtifactServiceProtocol(ServiceProtocol):
        """Protocol for artifact service."""
    
        async def get_artifact_info(self, artifact_name: str) -> str:
            """Get artifact information."""
            ...
  • Secondary tool dispatch/registration in HTTP transport mode handler.
    elif tool_name == "get_artifact_info":
        result = await artifact_service.get_artifact_info(arguments.get("artifact_name"))
Behavior3/5

Does 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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters4/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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.

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