Lodestar MCP Server
启动项目
uv venvuv syncmcp dev server.py每次修改代码后,都需要重新运行
mcp dev server.py注意:你应该研究如何进行热重载
查看其他示例 mcp 服务器如何运行及其各自的工具暴露
可能的后续步骤:
我应该将 API 密钥和其他内容公开为资源吗?还是应该将平台 URL 作为资源?
如果用户没有当前用户的 api_key 和 project_id,其中一个工具应该为用户获取新的 api_key 和 project_id,但我需要以某种方式获取正确的用户
看看其他工具是如何做的
编写 mcp 提示,帮助 llms 了解如何编写提示以获得最佳结果
要查看的服务器:
看起来其中大多数都在使用 ts,也许我也应该换个方式
实际上,我不明白为什么 TypeScript 会更好,特别是在我们的用例中,所以坚持使用可读性更好的 Python 可能会更好
潜在贡献
开发人员向 MCP 编写代码时的热重载
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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