Model Context Protocol (MCP) Server

Name: Model Context Protocol (MCP) Server
Author: infinyte

@infinyte

代码仓库

关于 Model Context Protocol (MCP) Server

simple mcp server

基本信息

分类

其他

运行时

node

传输方式

stdio

发布者

infinyte

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-server-infinyte": {
      "command": "docker",
      "args": [
        "compose",
        "up",
        "-d"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Model Context Protocol (MCP) Server?

A server that provides a unified API for multiple AI model providers (Anthropic, OpenAI), supporting chat completions, legacy completions, tool calling, and context handling. It is built for developers integrating AI models with persistent state management via MongoDB.

How to use Model Context Protocol (MCP) Server?

Clone the repository, run npm install, then npm run setup to configure API keys. Start the server with npm start; it will be available at http://localhost:3000. Use POST /mcp/:provider to make AI model requests and GET /tools/available to discover tools.

Key features of Model Context Protocol (MCP) Server

Unified API for Anthropic and OpenAI providers
Chat completions and legacy completions support
Tool calling with automatic execution
MongoDB persistence and state management
Environment-based configuration via .env file
Tool execution history and analytics tracking

Use cases of Model Context Protocol (MCP) Server

Build AI-powered chatbots with multiple model backends
Incorporate web search and content retrieval into AI responses
Generate and edit images using OpenAI or Stability AI
Enable AI agents to dynamically discover and call available tools

FAQ from Model Context Protocol (MCP) Server

Which AI providers are supported?

Anthropic (for Claude models) and OpenAI (for GPT models and DALL-E image generation) are supported.

How do I set up MongoDB?

MongoDB can be set up automatically with Docker via npm run setup-mongodb, manually with Docker Compose, installed locally, or used through MongoDB Atlas for production.