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Getting Started with MCP

This guide from MCPSERVER.WORK provides an introduction to the Model Context Protocol (MCP) ecosystem, helping you understand how to use MCP servers and clients for AI-powered applications.

What is MCP?​

The Model Context Protocol (MCP) is an open standard that enables AI models (like Claude, GPT, etc.) to interact with external resources through a standardized interface. MCP allows AI assistants to:

  • Access local files and databases
  • Browse the web and perform searches
  • Interact with APIs and services
  • Execute code and commands
  • Store and retrieve information

All of this happens through a secure, standardized protocol that mediates between the AI model and the external tool or resource.

Understanding MCP Components​

An MCP setup consists of three main components:

  1. MCP Client: The application that communicates with the AI model and MCP servers
  2. AI Model: The large language model (LLM) generating responses and using tools
  3. MCP Server: A service providing specific functionality (like file access, database queries, etc.)

Getting Started with MCP as a User​

If you're an end user who wants to leverage MCP capabilities with AI assistants, follow these steps:

Step 1: Choose an MCP Client​

Select an MCP client application that fits your needs. Some popular options include:

  • Claude Desktop: Official desktop application from Anthropic
  • Cursor: AI-powered code editor with MCP support
  • LibreChat: Open-source web interface for multiple AI models

See our Clients Directory for more options.

Step 2: Install MCP Servers​

Most MCP clients come with built-in servers for basic functionality, but you can extend capabilities by installing additional MCP servers:

  1. Visit our Servers Directory on MCPSERVER.WORK to browse available MCP servers
  2. Choose servers based on your needs (database access, API integration, etc.)
  3. Follow the installation instructions for each server (typically through Git)

Example installation of a typical MCP server:

# Clone the repository
git clone https://github.com/example/mcp-server-example

# Navigate to the directory
cd mcp-server-example

# Install dependencies
npm install  # or pip install -r requirements.txt for Python servers

# Start the server
npm start  # or python server.py for Python servers

Step 3: Configure Your MCP Client​

Once you have installed the MCP servers you need:

  1. Open your MCP client application
  2. Navigate to settings or configuration section
  3. Add the MCP servers you installed
  4. Typically, you'll need to provide:
    • Server name
    • Server URL or path
    • Authentication details (if required)

Step 4: Using MCP Tools with AI​

Now you can use your AI assistant with the extended capabilities provided by MCP:

  1. Start a new conversation in your MCP client
  2. Ask your AI assistant to use a specific tool or function
  3. The AI will use the appropriate MCP server to fulfill your request

Examples of MCP usage:

  • "Can you search for recent news about artificial intelligence?"
  • "Please analyze the data in this CSV file I'm uploading."
  • "Help me write a Python script and test it locally."
  • "Look up information about this company in our database."

Getting Started with MCP as a Developer​

If you're a developer who wants to build MCP servers or integrate MCP into your applications:

Building an MCP Server​

  1. Choose a Framework: Select an MCP framework in your preferred language:

  2. Define Your Tools: Implement the tools and functions your server will provide:

    • File operations
    • API integrations
    • Data processing
    • Custom utilities

  3. Implement Security: Ensure your server has proper authentication and security controls

  4. Test Your Server: Use tools like mcp-cli to test your server

Example: Simple MCP Server in TypeScript​

import { createServer } from 'litemcp';

const server = createServer({
  tools: [
    {
      name: 'hello_world',
      description: 'Returns a greeting message',
      parameters: {
        properties: {
          name: {
            type: 'string',
            description: 'Name of the person to greet',
          },
        },
        required: ['name'],
      },
      handler: async ({ name }) => {
        return `Hello, ${name}!`;
      },
    },
  ],
});

server.listen();

MCP Resources​

For more information, visit MCPSERVER.WORK

Next Steps​

By leveraging the Model Context Protocol, you can significantly enhance the capabilities of AI assistants, allowing them to interact with the digital world in more powerful and useful ways.

Visit MCPSERVER.WORK regularly for updates on new MCP servers and clients as the ecosystem grows.