Mcp Use Ts

# Create a new MCP application
npx create-mcp-use-app my-mcp-app

# Navigate to your project
cd my-mcp-app

# Start development with hot reload and auto-inspector
npm run dev

import { MCPClient, MCPAgent } from 'mcp-use'
import { ChatOpenAI } from '@langchain/openai'

// Configure MCP servers
const client = MCPClient.fromDict({
  mcpServers: {
    filesystem: {
      command: 'npx',
      args: ['@modelcontextprotocol/server-filesystem']
    },
    github: {
      command: 'npx',
      args: ['@modelcontextprotocol/server-github'],
      env: { GITHUB_TOKEN: process.env.GITHUB_TOKEN }
    }
  }
})

// Create an AI agent
const agent = new MCPAgent({
  llm: new ChatOpenAI({ model: 'gpt-4' }),
  client,
  maxSteps: 10
})

// Use the agent with natural language
const result = await agent.run(
  'Search for TypeScript files in the project and create a summary'
)

import { createMCPServer } from 'mcp-use/server'
import { z } from 'zod'

// Create your MCP server
const server = createMCPServer('weather-server', {
  version: '1.0.0',
  description: 'Weather information MCP server'
})

// Define tools with Zod schemas
server.tool('get_weather', {
  description: 'Get current weather for a city',
  parameters: z.object({
    city: z.string().describe('City name'),
    units: z.enum(['celsius', 'fahrenheit']).optional()
  }),
  execute: async ({ city, units = 'celsius' }) => {
    const weather = await fetchWeather(city, units)
    return {
      temperature: weather.temp,
      condition: weather.condition,
      humidity: weather.humidity
    }
  }
})

// Define resources
server.resource('weather_map', {
  description: 'Interactive weather map',
  uri: 'weather://map',
  mimeType: 'text/html',
  fetch: async () => {
    return generateWeatherMapHTML()
  }
})

// Start the server
server.listen(3000)
// 🎉 Inspector automatically available at http://localhost:3000/inspector
// 🚀 MCP endpoint at http://localhost:3000/mcp

import { streamEventsToAISDKWithTools } from 'mcp-use'
import { LangChainAdapter } from 'ai'

// In your Next.js API route
export async function POST(req: Request) {
  const { prompt } = await req.json()

  const streamEvents = agent.streamEvents(prompt)
  const enhancedStream = streamEventsToAISDKWithTools(streamEvents)
  const readableStream = createReadableStreamFromGenerator(enhancedStream)

  return LangChainAdapter.toDataStreamResponse(readableStream)
}

// resources/analytics-dashboard.tsx
import { useMcp } from 'mcp-use/react'

export default function AnalyticsDashboard() {
  const { callTool, status } = useMcp()
  const [data, setData] = useState(null)

  useEffect(() => {
    callTool('get_analytics', { period: '7d' })
      .then(setData)
  }, [])

  return (
    
  )
}

# Development with hot reload
mcp-use dev

# Production build
mcp-use build

# Start production server
mcp-use start

# Start development
mcp-use dev
# Server running at http://localhost:3000
# Inspector opened at http://localhost:3000/inspector
# Watching for changes...

# Make changes to your code
# Server automatically restarts
# UI widgets hot reload
# Inspector updates in real-time

server.listen(3000)
// Inspector at http://localhost:3000/inspector

npx mcp-inspect --url https://mcp.example.com/sse

import { mountInspector } from '@mcp-use/inspector'
mountInspector(app, '/debug')

# Interactive mode
npx create-mcp-use-app

# Direct mode
npx create-mcp-use-app my-app --template advanced

// Create an agent that can manage files
const agent = new MCPAgent({
  llm: new ChatOpenAI(),
  client: MCPClient.fromDict({
    mcpServers: {
      filesystem: {
        command: 'npx',
        args: ['@modelcontextprotocol/server-filesystem', '/Users/me/documents']
      }
    }
  })
})

// Natural language file operations
await agent.run('Organize all PDF files into a "PDFs" folder sorted by date')
await agent.run('Find all TypeScript files and create a project summary')
await agent.run('Delete all temporary files older than 30 days')

// Connect multiple MCP servers
const client = MCPClient.fromDict({
  mcpServers: {
    browser: { command: 'npx', args: ['@playwright/mcp'] },
    search: { command: 'npx', args: ['@mcp/server-search'] },
    memory: { command: 'npx', args: ['@mcp/server-memory'] }
  }
})

const researcher = new MCPAgent({
  llm: new ChatAnthropic(),
  client,
  useServerManager: true // Auto-select appropriate server
})

// Complex research task
const report = await researcher.run(`
  Research the latest developments in quantum computing.
  Search for recent papers, visit official websites,
  and create a comprehensive summary with sources.
`)

const server = createMCPServer('db-admin', {
  version: '1.0.0'
})

server.tool('execute_query', {
  description: 'Execute SQL query safely',
  parameters: z.object({
    query: z.string(),
    database: z.string()
  }),
  execute: async ({ query, database }) => {
    // Validate and execute query
    const results = await db.query(query, { database })
    return { rows: results, count: results.length }
  }
})

// Create an AI-powered DBA
const dba = new MCPAgent({
  llm: new ChatOpenAI({ model: 'gpt-4' }),
  client: new MCPClient({ url: 'http://localhost:3000/mcp' })
})

await dba.run('Show me all users who signed up this week')
await dba.run('Optimize the slow queries in the performance log')

my-mcp-app/
├── src/
│   └── index.ts          # MCP server definition
├── resources/            # UI widgets (React components)
│   ├── dashboard.tsx     # Main dashboard widget
│   └── settings.tsx      # Settings panel widget
├── package.json         # Dependencies and scripts
├── tsconfig.json        # TypeScript configuration
├── .env                 # Environment variables
└── dist/               # Build output
    ├── index.js        # Compiled server
    └── resources/      # Compiled widgets

# 1. Create your project
npx create-mcp-use-app my-project

# 2. Start development
cd my-project
npm run dev

# 3. Make changes - hot reload handles the rest
# 4. Test with the auto-opened inspector

# Build for production
npm run build

# Deploy with Docker
docker build -t my-mcp-server .
docker run -p 3000:3000 my-mcp-server

# Or deploy to any Node.js host
npm run start

# Create a changeset for your changes
pnpm changeset

# Version packages based on changesets
pnpm changeset version

# Publish all changed packages
pnpm changeset publish

# Publish individual packages
pnpm --filter mcp-use publish --access public
pnpm --filter @mcp-use/cli publish --access public
pnpm --filter @mcp-use/inspector publish --access public
pnpm --filter create-mcp-use-app publish --access public

# Or publish all at once
pnpm -r publish --access public

# Clone the repository
git clone https://github.com/mcp-use/mcp-use-ts.git
cd mcp-use-ts

# Install dependencies
pnpm install

# Build all packages
pnpm build

# Run tests
pnpm test

# Start development
pnpm dev