mcp-use

<h1 align="center">Unified MCP Client Library</h1> [](https://www.npmjs.com/package/@modelcontextprotocol/mcp-client) [](https://www.npmjs.com/package/@modelcontextprotocol/mcp-client) [](https://github.com/zandko/mcp-use/blob/main/LICENSE) [](https://eslint.org) [](https://github.com/zandko/mcp-use/stargazers) 🌐 **MCP Client** is the open-source way to connect **any LLM to any MCP server** in TypeScript/Node.js, letting you build custom agents with tool access without closed-source dependencies. 💡 Let developers easily connect any LLM via LangChain.js to tools like web browsing, file operations, 3D modeling, and more. --- ## ✨ Key Features | Feature | Description | | ------------------------------- | -------------------------------------------------------------------------- | | 🔄 **Ease of use** | Create an MCP-capable agent in just a few lines of TypeScript. | | 🤖 **LLM Flexibility** | Works with any LangChain.js-supported LLM that supports tool calling. | | 🌐 **HTTP Support** | Direct SSE/HTTP connection to MCP servers. | | ⚙️ **Dynamic Server Selection** | Agents select the right MCP server from a pool on the fly. | | 🧩 **Multi-Server Support** | Use multiple MCP servers in one agent. | | 🛡️ **Tool Restrictions** | Restrict unsafe tools like filesystem or network. | | 🔧 **Custom Agents** | Build your own agents with LangChain.js adapter or implement new adapters. | --- ## 🚀 Quick Start ### Installation ```bash # Install from npm npm install mcp-use # LangChain.js and your LLM provider (e.g., OpenAI) npm install langchain @langchain/openai dotenv ``` Create a `.env`: ```ini OPENAI_API_KEY=your_api_key ``` ### Basic Usage ```ts import { ChatOpenAI } from '@langchain/openai' import { MCPAgent, MCPClient } from 'mcp-use' import 'dotenv/config' async function main() { // 1. Configure MCP servers const config = { mcpServers: { playwright: { command: 'npx', args: ['@playwright/mcp@latest'] } } } const client = MCPClient.fromDict(config) // 2. Create LLM const llm = new ChatOpenAI({ modelName: 'gpt-4o' }) // 3. Instantiate agent const agent = new MCPAgent({ llm, client, maxSteps: 20 }) // 4. Run query const result = await agent.run('Find the best restaurant in Tokyo using Google Search') console.log('Result:', result) } main().catch(console.error) ``` --- ## 📂 Configuration File You can store servers in a JSON file: ```json { "mcpServers": { "playwright": { "command": "npx", "args": ["@playwright/mcp@latest"] } } } ``` Load it: ```ts import { MCPClient } from 'mcp-use' const client = MCPClient.fromConfigFile('./mcp-config.json') ``` --- ## 🔄 Multi-Server Example ```ts const config = { mcpServers: { airbnb: { command: 'npx', args: ['@openbnb/mcp-server-airbnb'] }, playwright: { command: 'npx', args: ['@playwright/mcp@latest'] } } } const client = MCPClient.fromDict(config) const agent = new MCPAgent({ llm, client, useServerManager: true }) await agent.run('Search Airbnb in Barcelona, then Google restaurants nearby') ``` --- ## 🔒 Tool Access Control ```ts const agent = new MCPAgent({ llm, client, disallowedTools: ['file_system', 'network'] }) ``` ## 📜 License MIT © [Zane](https://github.com/zandko)