Step-by-step guides to mastering the Anthropic Model Context Protocol (MCP). Learn MCP server setup, AI assistant integration, and build powerful AI applications.
Dive into our detailed tutorials to learn and implement the Model Context Protocol effectively. Whether you're a beginner setting up your first MCP server or an expert exploring advanced AI integrations, our guides provide practical knowledge and code examples to help you succeed with Anthropic MCP.
Get started quickly by creating a basic MCP server using the Python SDK. This tutorial provides a beginner-friendly walkthrough of the initial setup, installation of the necessary Python libraries, and running your first functional MCP server to serve simple context.
pip install model-context-protocolThis command installs the official Anthropic MCP Python SDK, enabling you to build MCP servers and clients in Python.
simple_mcp_server.py, and add the following code:
from mcp.server import MCPServer
from mcp.source import Source
class SimpleSource(Source):
async def get_context(self, request):
return {"content": "Hello from your simple MCP server!"}
async def main():
server = MCPServer(sources={"simple": SimpleSource()})
await server.start()
if __name__ == "__main__":
import asyncio
asyncio.run(main())
This Python code sets up a minimal MCP server with a simple source that returns a greeting message. It's perfect for understanding the basic structure of an MCP server.
simple_mcp_server.py and run:
python simple_mcp_server.pyExecuting this command starts your MCP server. It will run in the background, ready to respond to MCP client requests.
Extend your MCP server to access and provide context directly from files on your local file system. This tutorial outlines the steps and concepts for building a robust file system connector, enabling your AI applications to access local data securely and efficiently using the Model Context Protocol.
Source class to interact with the local file system using Python's file I/O operations. The example code below demonstrates how to read file content based on a requested filepath.
import os
from mcp.server import MCPServer
from mcp.source import Source
class FileSystemSource(Source):
def __init__(self, base_dir):
self.base_dir = base_dir
async def get_context(self, request):
filepath = request.get("filepath") # Assuming request contains filepath
if not filepath:
return {"error": "Filepath not provided"}
full_path = os.path.join(self.base_dir, filepath)
if not os.path.exists(full_path) or not os.path.isfile(full_path):
return {"error": "File not found"}
try:
with open(full_path, 'r') as file:
content = file.read()
return {"content": content, "filepath": filepath}
except Exception as e:
return {"error": f"Error reading file: {e}"}
async def main():
server = MCPServer(sources={"files": FileSystemSource(base_dir="/path/to/your/files")}) # Replace with actual path
await server.start()
if __name__ == "__main__":
import asyncio
asyncio.run(main())
This code snippet shows how to create an MCP Source that reads files from a specified base directory. Remember to handle file paths securely and validate user inputs.
FileSystemSource with the directory you want to grant access to. Important: Replace /path/to/your/files with a safe and appropriate directory path on your system.Get a conceptual understanding of how to integrate an MCP server with an AI assistant like Anthropic's Claude to enhance its context awareness. This tutorial provides a high-level overview of the integration process and demonstrates how MCP enables AI assistants to access external information for more informed and relevant responses.
Imagine interacting with Claude and wanting it to summarize a document from your file system that's connected via your MCP server.
Example User Prompt to Claude: "Claude, can you summarize the content of the document located at filepath: /documents/report.txt using the file system data source?"
Conceptual Workflow (Behind the Scenes):
/documents/report.txt, and sends the document content back to Claude.