Anthropic MCP Quickstart Guide

Anthropic MCP Quickstart: Your First MCP Server in Minutes

This quickstart guide will walk you through setting up a basic Anthropic MCP server and understanding the fundamentals of the Model Context Protocol. We'll be using Python for this guide, leveraging the Anthropic MCP Server SDK for Python, to demonstrate just how easy it is to get started with MCP and AI integration.

Prerequisites for setting up your MCP Server

Before you begin setting up your MCP server, ensure you have the following prerequisites installed on your system. Python is required for this quickstart as we will be using the Anthropic MCP Server SDK for Python:

• Python 3.7+: Required to run the MCP server and compatible with the Anthropic MCP Server SDK. You can download the latest version from python.org.
• pip: Python's package installer, is essential for installing the MCP Server SDK and is usually included with standard Python installations.

Step 1: Install the Anthropic MCP Server SDK for Python using pip

Begin by installing the necessary Anthropic MCP Server SDK. Open your terminal or command prompt and use pip, the Python package installer, to install the anthropic-mcp-server package. This SDK simplifies the process of building MCP servers in Python:

pip install anthropic-mcp-server

Step 2: Create a Simple MCP Server in Python

Now, let's create a basic MCP server that serves a simple text string as context. Create a new Python file named, for example, simple_mcp_server.py, and copy the following Python code into it. This code utilizes the anthropic-mcp-server library to quickly set up an MCP server with a single route:

from anthropic_mcp_server import MCPServer, RouteHandler, Request, Response

class SimpleRouteHandler(RouteHandler):
    async def handle_request(self, request: Request) -> Response:
        return Response(
            body_type="text",
            body={"text": "Hello from your simple MCP server!"},
        )

async def main():
    server = MCPServer()
    server.add_route("/simple-context", SimpleRouteHandler())
    await server.start()

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

Code Explanation:

• We start by importing the essential classes: MCPServer, RouteHandler, Request, and Response from the anthropic_mcp_server Python library.
• SimpleRouteHandler class is defined, inheriting from RouteHandler. This class is responsible for handling incoming requests to a specific route.
• The handle_request method within SimpleRouteHandler is the core logic. It takes a Request object as input and returns a Response object. Here, it creates a Response with:
  • body_type="text": Specifies that the response body is plain text.
  • body={"text": "Hello from your simple MCP server!"}: The actual text content of the response.
• The main() function sets up and starts the MCP server:
  • server = MCPServer(): An instance of MCPServer is created, initializing our MCP server.
  • server.add_route("/simple-context", SimpleRouteHandler()): A route is added to the server. Any requests to /simple-context will be handled by our SimpleRouteHandler.
  • await server.start(): This line starts the MCP server, making it ready to receive requests.
• The if __name__ == "__main__": block ensures that the main() function is executed when the script is run directly. It uses asyncio.run(main()) to run the asynchronous main() function.

Step 3: Run Your MCP Server using Python

With your simple_mcp_server.py file created, you are now ready to run your basic MCP server. Navigate to the directory where you saved the file in your terminal or command prompt. Then, execute the Python script using the command below. This will start the MCP server, typically on http://localhost:8080, ready to serve context via the Model Context Protocol.

python simple_mcp_server.py

Upon successful execution, you should observe output in your terminal indicating that the server has started and is running, usually specifying the address http://localhost:8080.

Step 4: Test Your MCP Server and Verify the Response

To confirm that your MCP server is functioning correctly and serving context as expected, you can easily test it by sending a request to the defined route. Open your preferred web browser or utilize a command-line tool like curl to access the following URL. This will send a request to your running MCP server at the /simple-context endpoint:

http://localhost:8080/simple-context

If your MCP server is running properly, you should receive the following text response in your browser or curl output. This JSON response confirms that your server is successfully serving context in the expected MCP format:

{"type":"text","text":"Hello from your simple MCP server!"}

Congratulations! This successful JSON response confirms that your basic MCP server is up and running and is capable of serving context through the Model Context Protocol. You have successfully completed the initial setup of an MCP server!

Step 5: Explore Advanced MCP Features and Integration Options

Excellent work! You have now established a foundational MCP server. To further enhance your understanding and capabilities with Anthropic MCP, here are recommended next steps and areas to explore for deeper integration and more complex use cases:

1. Explore Integrating with Diverse Data Sources: Databases, APIs, and Files:
   • Extend the functionality of your SimpleRouteHandler to dynamically fetch data from various sources. Instead of serving a static string, modify it to retrieve data from local files, connect to databases, or interact with external APIs.
   • Consult the comprehensive Documentation for detailed guidance on implementing different types of RouteHandler classes and strategies for seamless integration with a wide range of data sources.
2. Deep Dive into Request Handling and Parameter Processing:
   • Examine the Request object within the handle_request method more closely. Understand how to effectively process incoming requests, extract parameters, and utilize this information to tailor your server's responses dynamically.
3. Understand and Implement Robust Security Considerations:
   • Security is paramount when exposing data through an MCP server. Thoroughly review the Security section on the FireMCP website and refer to the official MCP documentation to gain a deep understanding of security best practices. Implement appropriate security measures to protect your data and MCP server.
4. Integrate with AI Assistants and LLM Applications:
   • While this quickstart focused on server setup, the true power of MCP is realized when integrated with AI assistants and Large Language Model (LLM) applications. Configure AI assistants, such as Anthropic's Claude or custom applications built with AI models, to utilize your MCP server's endpoint (e.g., http://localhost:8080/simple-context). This enables these AI systems to fetch real-time context from your server when needed, enhancing their responses and capabilities. Refer to the specific documentation of your AI assistant or platform for instructions on integrating with external context providers like MCP.
5. Engage with the Growing MCP Community and Contribute:
   • Become an active member of the FireMCP community! Visit the Community Forum to ask questions, share your insights and projects, collaborate with other developers, and learn from the collective experiences of the MCP community.

This quickstart guide provides a solid foundation for your journey with Anthropic MCP. Remember, this is just the initial step. MCP is a versatile and powerful protocol designed to unlock the full potential of AI by enabling seamless access to external knowledge and tools. We encourage you to explore the comprehensive documentation, engage with community resources, and continue experimenting to fully harness the power of MCP for your AI-driven applications!