Lodestar MCP Server
프로젝트를 시작하려면
지엑스피1
uv syncmcp dev server.py코드를 변경할 때마다 다시 실행해야 합니다.
mcp dev server.py참고: 핫 리로드 방법을 살펴보세요.
다른 예제 mcp 서버가 각각의 도구를 노출하여 어떻게 실행되는지 살펴보세요.
다음 단계:
API 키와 기타 항목을 리소스로 노출해야 합니까? 아니면 플랫폼 URL을 리소스로 만들어야 합니까?
도구 중 하나는 현재 사용자에게 api_key와 project_id가 없는 경우 해당 사용자의 새 api_key와 project_id를 가져오는 것이어야 하지만 어떻게든 올바른 사용자를 가져와야 합니다.
다른 도구가 이 작업을 어떻게 수행하는지 살펴보세요.
LLM이 최상의 결과를 얻기 위해 프롬프트를 작성하는 방법을 이해하는 데 도움이 되는 MCP 프롬프트를 작성하세요.
살펴볼 서버:
대부분이 ts를 사용하는 것 같은데, 나도 그걸로 바꿔야 할 것 같아요.
사실 저는 TypeScript가 특히 우리의 사용 사례에서 더 나을 이유를 모르겠습니다. 따라서 더 나은 가독성을 고수하는 Python이 더 나을 수도 있습니다.
잠재적 기여
개발자가 MCP에 코드를 작성할 때 핫 리로드
Available Tools
1 tooldoc_queryC
Query project documentation.
Args:
query (str): The query text from the user
api_key (str): Authentication key for the request
project_id (str): Identifier for the target project
Returns:
ContextResponse: The generated response
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| api_key | Yes | ||
| project_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions authentication via api_key and returns a ContextResponse, but fails to explain what the tool does beyond 'query' (e.g., how it processes queries, any rate limits, error handling, or what ContextResponse entails). This leaves significant gaps in understanding its behavior.
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 structured with clear sections for Args and Returns, making it easy to parse. It's front-loaded with the purpose statement and avoids unnecessary fluff. However, the parameter explanations are very brief and could be more informative without sacrificing conciseness.
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 complexity of a query tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain the return value (ContextResponse is undefined), lacks details on query processing or limitations, and provides minimal parameter guidance, making it inadequate for full understanding.
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 must compensate. It lists all three parameters with brief explanations (e.g., 'query text from the user'), adding basic semantics beyond the schema's titles. However, it doesn't provide details like format constraints, examples, or deeper meaning, offering only minimal compensation for the lack of schema descriptions.
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 the tool 'Query project documentation' which provides a clear verb ('Query') and resource ('project documentation'), establishing its basic purpose. However, it lacks specificity about what kind of querying it performs (e.g., semantic search, keyword matching, retrieval) and doesn't differentiate from siblings since there are none, making it somewhat vague.
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, such as prerequisites, typical use cases, or alternatives. With no sibling tools, there's no need for differentiation, but it still lacks any context about appropriate scenarios or limitations, leaving usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'doc_query' has a clearly defined purpose that cannot be confused with any other tool in the set.
The single tool name 'doc_query' follows a clear verb_noun pattern. With only one tool, the naming is inherently consistent as there are no other tools to compare against or create inconsistencies with.
A single tool feels thin for a documentation query server, suggesting limited functionality. While it might be appropriate for a minimal prototype, a production documentation server would typically offer multiple operations like search, browse, filter, or manage documentation, making this count borderline inadequate.
The tool surface is severely incomplete for a documentation domain. While 'doc_query' covers querying, there are obvious gaps: no tools for listing available documentation, managing documentation sets, filtering by categories, or handling documentation updates. This will likely cause agent failures when trying to perform comprehensive documentation tasks.
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