Provides file reading and writing capabilities, allowing AI agents to interact with local file systems through dedicated file management tools
Enables system command execution and Python-based tool development for creating custom AI-accessible functionality
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Learning Projectshow me the contents of the README.md file"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Learning Project
What is MCP (Model Context Protocol)?
MCP is a protocol that enables AI models to interact with external tools and data sources. It's like giving AI a set of "hands" to work with your computer, files, databases, and APIs.
Related MCP server: MCP Local File Reader
Project Structure
mcp/
├── README.md # This file - project documentation
├── requirements.txt # Python dependencies
├── server.py # Main MCP server implementation
├── tools/ # Custom tools that AI can use
│ ├── __init__.py
│ ├── file_tools.py # File reading/writing tools
│ └── system_tools.py # System command tools
├── config/ # Configuration files
│ └── server_config.json
└── examples/ # Example usage and testing
└── test_client.pyLearning Objectives
By building this project, you'll learn:
MCP Architecture: How AI models communicate with external tools
Tool Development: Creating custom tools for AI to use
Protocol Implementation: Understanding the MCP specification
AI Integration: How to connect AI models with real-world data
Step-by-Step Learning Path
Setup & Dependencies - Install required packages
Basic Server - Create a minimal MCP server
File Tools - Build tools for reading/writing files
System Tools - Create tools for executing commands
Testing - Learn how to test your MCP server
Integration - Connect with AI models
Getting Started
# Install dependencies
pip install -r requirements.txt
# Run the server
python server.pyNext Steps
Follow along as we build each component step by step!
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