企业微信 & 飞书 OpenAPI 文档聚合 MCP 插件

Created By
wxkingstar8 months ago
本插件为 Model Context Protocol(MCP)提供了企业级 OpenAPI 文档能力,自动聚合并标准化 企业微信 与 飞书 等的开放接口文档。 通过统一上下文调用、搜索和补全,开发者可在无需频繁查阅官网文档的情况下,快速获取接口定义、请求参数、响应结构及权限要求。 适用于构建企业内部集成、自动化工作流、Bot/Agent 开发等高频 API 使用场景。
Overview

What is Doc Hub MCP?

Doc Hub MCP is a plugin that provides enterprise-level OpenAPI documentation capabilities for the Model Context Protocol (MCP), automatically aggregating and standardizing the open interface documentation of WeChat Work and Feishu.

How to use Doc Hub MCP?

To use Doc Hub MCP, install the dependencies using npm install, start the service with npm run mcp:server --silent, and integrate it with supported IDEs or AI programming assistants.

Key features of Doc Hub MCP?

  • Keyword search for matching titles and content.
  • Full text retrieval of Markdown documents.
  • Configurable namespace and document root directory.
  • Standard MCP compatibility for use with various AI tools.

Use cases of Doc Hub MCP?

  1. Quickly retrieving API documentation for WeChat Work and Feishu.
  2. Integrating with AI programming assistants for automated documentation queries.
  3. Supporting internal documentation and private API documentation retrieval.

FAQ from Doc Hub MCP?

  • Can Doc Hub MCP be used offline?

Yes! It supports offline retrieval of Markdown documents.

  • Is there a limit to the number of documents that can be aggregated?

The initial setup may take time due to the size of the documentation, but it can handle a large number of documents.

  • How do I configure the document root?

You can specify the document root using the DOC_ROOT environment variable.

Server Config

{
  "mcpServers": {
    "search-docs": {
      "command": "npx",
      "args": [
        "-y",
        "doc-hub-mcp@latest"
      ]
    }
  }
}
Project Info
Created At
8 months ago
Updated At
8 months ago
Author Name
wxkingstar
Star
-
Language
-
License
-

Recommend Servers

View All
Mnemom

14 hours ago
//beforeyouship — LLM Cost Modeling From Your Editor
@Indiegoing

Query realistic LLM cost models without leaving your editor. beforeyouship models the **true monthly cost** of an LLM app architecture — retries, prompt caching, batch discounts, infra overhead, and 3×/10× growth — across GPT-5.x, Claude, Gemini, DeepSeek, and more. Not a token calculator: a planning tool for the design phase, before you commit to a stack. **No API key needed to try it** — demo mode covers the six free-tier models. A Pro key from [beforeyouship.dev](https://beforeyouship.dev) unlocks the full 18-model catalog. ## What you can ask - "How much will a RAG chatbot cost at 10,000 requests/day?" - "Compare Claude Haiku vs Gemini Flash pricing for my workload" - "What's the cheapest model for a multi-step agent at scale?" - "Show me current per-token prices for Anthropic models" ## Tools ### `estimate_cost` Full cost model for an architecture at a given usage level. Returns Naive / Realistic / Worst Case monthly cost per model, 3×/10× growth scenarios, and an opinionated recommendation with reasoning. ### `get_model_prices` Current per-1M-token pricing — input, output, cached input, batch — with context windows and staleness metadata. ### `list_archetypes` Seven preset architecture patterns (simple chatbot, chatbot with history, RAG pipeline, multi-model router, coding assistant, document processor, multi-step agent) used as starting points for estimates. ## Setup **Claude Code:** ​```bash claude mcp add --transport http beforeyouship https://beforeyouship.dev/api/mcp ​``` **Cursor / other clients** — add a remote server: ​```json { "mcpServers": { "beforeyouship": { "type": "streamable-http", "url": "https://beforeyouship.dev/api/mcp" } } } ​``` Add an `Authorization: Bearer bys_...` header with a Pro key for the full catalog. ## Try it > Estimate the monthly cost of a RAG pipeline at 10,000 requests/day

13 hours ago
Docwand

13 hours ago