MCP Tutorial (Clearly Explained) - Claude Desktop, Perplexity, Notion & Filesystem

Introduction

This tutorial provides a comprehensive overview of the Model Context Protocol (MCP), which facilitates efficient communication between AI models and applications. We will explore MCP architecture, how to access various MCP servers, and practical applications such as integrating with Notion and Perplexity. By the end of this guide, you will understand how to leverage MCP in your projects.

Step 1: Understand What MCP Is

• MCP is a protocol designed to streamline interactions between AI models and their environments.
• It allows for better context management, enabling AI to handle tasks more efficiently.
• Visit the MCP Introduction for detailed insights.

Step 2: Explore MCP Architecture

• The MCP architecture consists of several layers that manage data flow and context.

Key components include

• Client Layer: Where user requests originate.
• Server Layer: Processes requests and provides responses.
• Data Layer: Manages the data used by AI models.
• Understanding this structure is crucial for implementing MCP effectively.

Step 3: Accessing MCP Servers

• To utilize MCP, you need to connect to specific servers designed for this protocol.
• Check the awesome MCP servers list to find available servers.

Steps to access

• Select a server that suits your needs.
• Follow the provided documentation for installation and setup.

Step 4: Setting Up a Filesystem MCP Server

• A Filesystem MCP server allows for local data management and interaction.

Steps to set up

• Download the server software from the repository.
• Follow the instructions to install it on your local machine.
• Configure the server based on your requirements.

Step 5: Integrating Notion with MCP

• Notion can be enhanced using MCP for better documentation and organization.

Steps

• Connect your Notion workspace to the MCP server.
• Create a new database or page in Notion.
• Use API requests to send and receive data from MCP, allowing for dynamic content updates.

Step 6: Using Perplexity's MCP Server

• Perplexity provides an MCP server that assists in various data retrieval tasks.

Steps to utilize

• Access the Perplexity MCP server through its API.
• Formulate requests to gather data for analysis or review.
• Process the returned data as needed for your applications.

Step 7: Extracting Reviews from Your Local System

• You can retrieve reviews or other data from your filesystem using MCP.

Steps

• Ensure your Filesystem MCP server is running.
• Write a script or use a command line to query your local files.
• Example code snippet:

  import requests
  
  response = requests.get('http://localhost:5000/get-reviews')
  reviews = response.json()
  print(reviews)
  

Step 8: Conducting Competitor Price Analysis with Perplexity

• Leverage Perplexity’s capabilities for competitive analysis.

Steps

• Use the MCP server to pull competitor pricing data.
• Analyze the data for trends and insights.
• Create visualizations if necessary to present findings.

Step 9: Creating a Stylized Document in Notion from Claude

• You can generate documents in Notion using Claude’s capabilities.

Steps

• Prepare your content using Claude’s API.
• Send the formatted content to Notion using API calls.
• Example code snippet:

  notion_data = {
      "parent": {"database_id": "your_database_id"},
      "properties": {
          "Title": {"title": [{"text": {"content": "Your Title"}}]}
      },
      "children": [{"text": "Your Content"}]
  }
  response = requests.post('https://api.notion.com/v1/pages', json=notion_data)
  

Conclusion

In this tutorial, we covered the essentials of the Model Context Protocol, including its architecture, accessing MCP servers, and practical applications with Notion and Perplexity. By understanding and implementing MCP, you can enhance your AI projects significantly. For further exploration, consider diving deeper into each server’s documentation and experimenting with different integrations.