ContextMCP

Name: ContextMCP
Author: dodopayments

@dodopayments

代码仓库主页

关于 ContextMCP

Self-hosted MCP server for your documentation

基本信息

分类

其他

许可证

Apache-2.0

运行时

node

发布者

dodopayments

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"
      }
    }
  }
}

工具

未检测到工具

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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

FAQ from ContextMCP

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.