What is MCP: An Overview of the Model Context Protocol

The Model Context Protocol (MCP) is an open standard proposed by Anthropic to streamline how AI applications interact with tools and data sources. Designed to function as a universal interface, MCP simplifies the development and integration of AI-powered workflows, much like a USB port standardizes the connection of peripheral devices to computers.

By using MCP, developers avoid creating custom integrations for every external tool or data source, enabling AI applications to access a vast range of functionalities with minimal setup.

Components of MCP

To understand how MCP works, it helps to break down its architecture into the following core components, also known as the HCS (Host, Client, Server) architecture:

• Host: The application (often a language model) that requires access to data or tools. Examples of hosts include applications like AI-driven chatbots, cloud-based IDEs, or desktop apps.
• Client: The mediator that implements the MCP protocol, enabling a host to communicate with an MCP server.
• Server: A lightweight program that provides data or functionalities in a standardized way.

This architecture allows seamless interaction between hosts and servers through a client that acts as an intermediary.

Building MCP Servers with n8n

You can create your own MCP server using n8n, a popular no-code automation tool. Here's a step-by-step guide:

This no-code approach allows anyone to build an MCP server without programming expertise.

Code Implementation of MCP Servers

For more control over your MCP server, you can write code. Here’s a Python example of a custom MCP server that interacts with Google Sheets.

from flask import Flask, request, jsonify

app = Flask(__name__)

# Example Tool: List Spreadsheets
@app.route('/tools/list_spreadsheets', methods=['POST'])
def list_spreadsheets():
    return jsonify({
        "tools": ["Spreadsheet1", "Spreadsheet2", "Spreadsheet3"]
    })

# Example Resource: Read-Only Data
@app.route('/resources/get_spreadsheet', methods=['GET'])
def get_spreadsheet():
    # Example of a static resource
    spreadsheet_data = {"id": 1, "name": "Test Spreadsheet", "data": [[1, 2], [3, 4]]}
    return jsonify(spreadsheet_data)

# Example Prompt Template
@app.route('/prompts/analyze_data', methods=['POST'])
def analyze_data():
    data = request.json
    result = f"Analyzed data for: {data['spreadsheet_name']}."
    return jsonify({"response": result})

if __name__ == '__main__':
    app.run(port=5001)

Testing Your Server Locally

1. Save the above code in a file named mcp_server.py.
2. Run the file using the command:
   bashCopy

   python mcp_server.py

3. Use tools like curl or Postman to make requests to http://localhost:5001/tools/list_spreadsheets, http://localhost:5001/resources/get_spreadsheet, and other endpoints you define in your server.

The Python approach allows adding advanced functionality, such as resources and prompt templates, to make your server versatile.

Common Mistakes and Troubleshooting MCP Servers

Official reference: Model Context Protocol documentation.