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MCP Agent Kit

@dominiquekossi

リポジトリ

基本情報

カテゴリ

AI とエージェント

トランスポート

stdio

作者

dominiquekossi

投稿者

dom s

MCP Agent Kit について

a complete and intuitive SDK for building MCP Servers, MCP Agents, and LLM integrations (OpenAI, Claude, Gemini) with minimal effort.

#mcp-agent-kit#sdk#ai-agents

MCP Agent Kit の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-agent-kit": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-agent-kit"
      ],
      "env": {
        "MCP_SERVER_NAME": "my-server",
        "MCP_PORT": "7777",
        "LOG_LEVEL": "info",
        "OPENAI_API_KEY": "sk-...",
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "GEMINI_API_KEY": "...",
        "OLLAMA_HOST": "http://localhost:11434"
      }
    }
  }
}

MCP Agent Kit のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

概要

What is Mcp Agent Kit?

Mcp Agent Kit is a TypeScript package that simplifies creating MCP (Model Context Protocol) servers, AI agents, intelligent LLM routers, chatbots with conversation memory, and HTTP API helpers with built-in retry and timeout. It supports multiple LLM providers and is designed for developers who want to quickly build production-ready agents and tools.

How to use Mcp Agent Kit?

Install the package via npm (npm install mcp-agent-kit), then use functions like createAgent, createMCPServer, createChatbot, and createLLMRouter to build components. Configuration is optional via environment variables or code parameters. Examples are available in the /examples directory and can be run with npx ts-node.

Key features of Mcp Agent Kit

  • Zero config with smart defaults
  • Multi-LLM provider support (OpenAI, Anthropic, Gemini, Ollama)
  • Full TypeScript support and autocomplete
  • Built-in retry, timeout, and error handling
  • One-line setup for complex features (agents, servers, chatbots)
  • Extensible with custom providers and middleware

Use cases of Mcp Agent Kit

  • Create an MCP server to expose tools and resources to clients
  • Build an AI agent with function calling and system prompts
  • Develop a chatbot with automatic conversation memory and history limits
  • Route requests to different LLMs based on input length or content rules
  • Simplify HTTP requests with automatic retry and configurable timeout

よくある質問

1

Which LLM providers are supported?

OpenAI, Anthropic, Gemini, and Ollama are supported out of the box.

2

Do I need API keys for all providers?

API keys are required for OpenAI, Anthropic, and Gemini. Ollama runs locally and does not need an API key.

3

Can I use WebSocket transport for MCP servers?

Yes – call `server.start("websocket")` to start the server on WebSocket instead of stdio.

4

How do I configure environment variables?

Set variables like `OPENAI_API_KEY` or `LOG_LEVEL` in a `.env` file or in your environment. The package automatically loads `.env` files using dotenv.

5

What is the default transport for MCP servers?

The default transport is stdio, unless you specify `"websocket"` as an argument to `start()`.

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