Python Docs Server
python-docs-server MCP 服务器
模型上下文协议服务器
这是一个基于 TypeScript 的 MCP 服务器,它提供使用 Brave Search API 获取 Python 文档的工具。
特征
工具
get_python_docs- 获取给定查询的 Python 文档将搜索查询作为必需参数
使用 Brave Search API 获取相关文档链接
Related MCP server: MCP2Brave
发展
安装依赖项:
npm install构建服务器:
npm run build对于使用自动重建的开发:
npm run watch安装
要与 Claude Desktop 一起使用,请添加服务器配置:
在 MacOS 上: ~/Library/Application Support/Claude/claude_desktop_config.json在 Windows 上: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"python-docs-server": {
"command": "/path/to/python-docs-server/build/index.js"
}
}
}调试
由于 MCP 服务器通过 stdio 进行通信,调试起来可能比较困难。我们推荐使用MCP Inspector ,它以包脚本的形式提供:
npm run inspector检查器将提供一个 URL 来访问浏览器中的调试工具。
Available Tools
1 toolget_python_docsC
Get Python documentation for a given query
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query for Python documentation |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It mentions 'Get Python documentation' but doesn't specify aspects like data sources, rate limits, authentication needs, or response formats, leaving significant gaps in understanding the tool's 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 extremely concise and front-loaded, consisting of a single, clear sentence that directly states the tool's function without any unnecessary words or structural fluff.
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 lack of annotations and output schema, the description is incomplete for effective tool use. It fails to address key contextual elements like the source of documentation, result format, or any operational constraints, making it insufficient despite the simple parameter schema.
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 100%, with the single parameter 'query' fully documented in the schema. The description adds no additional meaning beyond what the schema provides, such as query examples or format details, so it meets the baseline for adequate but unenriched parameter information.
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 with a specific verb ('Get') and resource ('Python documentation'), and specifies the action is for a 'given query'. However, with no sibling tools mentioned, there's no opportunity to distinguish from alternatives, preventing a perfect score.
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 or any contextual prerequisites. It simply restates the basic functionality without indicating scenarios, limitations, or comparisons to other methods.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
- First observed
get_python_docs
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined as retrieving Python documentation, making it distinct by default.
The single tool name 'get_python_docs' follows a clear verb_noun pattern (get + python_docs). Since there is only one tool, consistency is inherently perfect with no deviations to assess.
A single tool is too few for a server named 'Python Docs Server', which suggests a broader scope like searching, browsing, or managing documentation. One tool feels thin and limits functionality, indicating a mismatch with the apparent purpose.
The tool set is severely incomplete for a documentation server. It only provides retrieval ('get_python_docs'), lacking essential operations such as search, list topics, get specific versions, or navigate documentation structure, which are typical for such a domain.
Maintenance
Related MCP Connectors
An MCP server that gives your AI access to the source code and docs of all public github repos
MCP server for agentverse documentation, generated by doc2mcp.
MCP server for accessing curated awesome list documentation
Related MCP Servers
- -licenseNot gradedqualityAmaintenanceAn MCP server implementation that integrates the Brave Search API, providing both web and local search capabilities.33,482 npm90,939MIT
- AlicenseCqualityDmaintenanceA server based on the MCP protocol that uses the Brave API for web search functionality.63MIT
- AlicenseNot gradedqualityDmaintenanceAn MCP server that integrates the Brave Search API to provide both web and local search capabilities, with features like pagination, filtering, and smart fallbacks.16MIT
- AlicenseNot gradedqualityDmaintenanceAn MCP server that enables AI assistants to access up-to-date documentation for Python libraries like LangChain, LlamaIndex, and OpenAI through dynamic fetching from official sources.1MIT