ContextMCP MCP Server

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基本信息

分类

其他

许可证

Apache-2.0

作者

dodopayments

关于 ContextMCP MCP Server

Self-hosted MCP server for your documentation

ContextMCP MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "context-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "contextmcp",
        "init"
      ],
      "env": {
        "PINECONE_API_KEY": "your_pinecone_key",
        "OPENAI_API_KEY": "your_openai_key"
      }
    }
  }
}

ContextMCP MCP Server 工具

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当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is ContextMCP?

ContextMCP is a self-hosted MCP server that indexes documentation from multiple sources and serves it via the Model Context Protocol (MCP) and a REST API. It creates a searchable knowledge base that AI assistants can query, making documentation accessible to LLMs.

How to use ContextMCP?

Scaffold a new project with npx contextmcp init, then configure your Pinecone API key and an embedding provider key (OpenAI or Gemini) in the .env file. Edit config.yaml to specify your documentation sources, run npm run reindex to index, and deploy the Cloudflare Worker with npm run deploy. The server is then ready to accept MCP queries.

Key features of ContextMCP

Self‑hosted MCP server for documentation indexing
Supports MDX/JSX, plain Markdown, and OpenAPI/Swagger specs
Chunks, embeds, and stores content in Pinecone vector database
Searchable via both MCP and REST API
Works with OpenAI or Gemini for embeddings
Includes a CLI scaffolding tool (npx contextmcp)

Use cases of ContextMCP

Allow AI assistants to query your product documentation directly
Add a searchable knowledge base to documentation sites like Mintlify or Docusaurus
Power a drop‑in “Ask AI” chat widget using the companion ContextChat
Provide API reference search via OpenAPI specs

常见问题

What documentation sources are supported?

ContextMCP can parse MDX/JSX (e.g., Mintlify, Fumadocs, Docusaurus), plain Markdown files (READMEs, CHANGELOGs), and OpenAPI/Swagger specifications.

Which embedding providers are available?

You can use either OpenAI (by setting `OPENAI_API_KEY`) or Gemini (by setting `GEMINI_API_KEY`) as the embedding provider in the `.env` file.

Where is the indexed data stored?

All embeddings and content chunks are stored in a Pinecone vector database. You need to provide your own `PINECONE_API_KEY`.

What are the system requirements?

Node.js 18 or later is required to run the CLI and build the project.

Can I add a chat UI to my docs?

Yes. Pair ContextMCP with ContextChat, a single Cloudflare Worker that queries the `/search` endpoint and streams answers with inline citations. It can be added to any site with one `<script>` tag.

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