Atlas-Docs
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Atlas-Docs search the React documentation for hooks"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
文档处理服务器 Atlas Docs
Atlas Docs MCP服务器为AI助手提供库和框架的技术文档,将官方文档处理为适合LLM使用的Markdown版本,适用于Cursor、Cline、Windsurf等MCP兼容的LLM客户端。 The Atlas Docs MCP server provides technical documentation of libraries and frameworks for AI assistants, processes the official documentation into Markdown versions suitable for LLMS, and is applicable to McP-compatible LLM clients such as Cursor, Cline, Windsurf, etc.## 工具列表 Tool List
本MCP服务封装下列工具,可让模型通过标准化接口调用以下功能。 本MCP服务封装下列工具,可让模型通过标准化接口调用以下功能。
工具 Tool | 描述 Description |
list_docs | Lists all available documentation libraries and frameworks. Use this as your first step to discover available documentation sets. Returns name, description and source url for each documentation set. Required before using other documentation tools since you need the docName. |
search_docs | Searches a documentation set for specific content. Use this to find pages containing particular keywords, concepts, or topics. Returns matching pages ranked by relevance with their paths and descriptions. Follow up with get_docs_page to get full content. |
get_docs_index | Retrieves a condensed, LLM-friendly index of the pages in a documentation set. Use this for initial exploration to understand what's covered and identify relevant pages. Returns a markdown page with a list of available pages. Follow up with get_docs_page to get full content. |
get_docs_page | Retrieves a specific documentation page's content using its relative path. Use this to get detailed information about a known topic, after identifying the relevant page through get_docs_index or search_docs. Returns the complete content of a single documentation page. |
get_docs_full | Retrieves the complete documentation content in a single consolidated file. Use this when you need comprehensive knowledge or need to analyze the full documentation context. Returns a large volume of text - consider using get_docs_page or search_docs for targeted information. |
检查服务 ## Inspector
工具在线测试: https://mcp.xiaobenyang.com/inspector/1777316659387395
Online Tool test https://mcp.xiaobenyang.com/inspector/1777316659387395
Related MCP server: Documentation Retrieval MCP Server (DOCRET)
服务配置 MCP Server Config
如何获取 XBY-APIKEY ? How to get XBY-APIKEY ?
访问小笨羊科技网站 https://xiaobenyang.com,注册用户即可获得APIKEY Visit XiaoBenYang website https://xiaobenyang.com, register and get the APIKEY.
SSE
{
"mcpServers": {
"文档处理服务器": {
"headers": {
"XBY-APIKEY": "<YOUR_XBY_APIKEY>"
},
"type": "sse",
"url": "https://mcp.xiaobenyang.com/1777316659387395/sse"
}
}
}STREAMABLE HTTP
{
"mcpServers": {
"文档处理服务器": {
"headers": {
"XBY-APIKEY": "<YOUR_XBY_APIKEY>"
},
"type": "streamable_http",
"url": "https://mcp.xiaobenyang.com/1777316659387395/mcp"
}
}
}STDIO
{
"mcpServers": {
"文档处理服务器": {
"command": "npx",
"args": [
"-y",
"xiaobenyang-mcp"
],
"env": {
"XBY_APIKEY": "<YOUR_XBY_APIKEY>",
"mcpId": "1777316659387395",
},
"transport": "stdio"
}
}
}
Available Tools
5 toolsget_docs_fullget_docs_fullA
Retrieves the complete documentation content in a single consolidated file. Use this when you need comprehensive knowledge or need to analyze the full documentation context. Returns a large volume of text - consider using get_docs_page or search_docs for targeted information.
Inputs: docName (required).
get_docs_indexget_docs_indexA
Retrieves a condensed, LLM-friendly index of the pages in a documentation set. Use this for initial exploration to understand what's covered and identify relevant pages. Returns a markdown page with a list of available pages. Follow up with get_docs_page to get full content.
Inputs: docName (required).
get_docs_pageget_docs_pageA
Retrieves a specific documentation page's content using its relative path. Use this to get detailed information about a known topic, after identifying the relevant page through get_docs_index or search_docs. Returns the complete content of a single documentation page.
Inputs: docName (required), pagePath (required).
list_docslist_docsA
Lists all available documentation libraries and frameworks. Use this as your first step to discover available documentation sets. Returns name, description and source url for each documentation set. Required before using other documentation tools since you need the docName.
Inputs: no named parameters.
search_docssearch_docsA
Searches a documentation set for specific content. Use this to find pages containing particular keywords, concepts, or topics. Returns matching pages ranked by relevance with their paths and descriptions. Follow up with get_docs_page to get full content.
Inputs: docName (required), query (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose with no overlap: list_docs discovers documentation sets, get_docs_index provides an overview, get_docs_page retrieves specific content, get_docs_full gets everything, and search_docs finds keywords. The descriptions explicitly guide usage, making misselection unlikely.
All tools follow a consistent verb_noun pattern with snake_case: list_docs, get_docs_index, get_docs_page, get_docs_full, and search_docs. The naming is predictable and readable, using clear verbs like 'get', 'list', and 'search' paired with descriptive nouns.
With 5 tools, the count is well-scoped for a documentation server, covering discovery, navigation, retrieval, and search without being overwhelming. Each tool earns its place by addressing a specific need in the documentation workflow, from initial exploration to detailed content access.
The tool set provides complete coverage for documentation access: list_docs for discovery, get_docs_index for navigation, get_docs_page for targeted retrieval, get_docs_full for comprehensive content, and search_docs for keyword-based finding. There are no obvious gaps, and the descriptions guide agents through a logical workflow.
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