Hands-On AI Agents MCP
this repo is for linkedin learning course: Hands-On AI: Building AI Agents with Model Context Protocol (MCP) and Agent2Agent (A2A)
What is Hands-On AI Agents MCP?
Hands-On AI Agents MCP is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to this repo is for linkedin learning course: hands-on ai: building ai agents with model context protocol (mcp) and agent2agent (a2a)
this repo is for linkedin learning course: Hands-On AI: Building AI Agents with Model Context Protocol (MCP) and Agent2Agent (A2A)
This server falls under the Developer Tools and Coding Agents categories on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- this repo is for linkedin learning course: Hands-On AI: Buil
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx hands-on-ai-building-ai-agents-with-model-context-protocol-mcp-and-agent2agent-a2a-6055298Configuration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Hands-On AI Agents MCP
This repository contains the companion code for the LinkedIn Learning course 'Hands-On AI: Building AI Agents with Model Context Protocol (MCP) and Agent2Agent (A2A)' taught by Kumaran Ponnambalam. It provides working Python examples that demonstrate MCP server and client architecture, A2A multi-agent communication patterns, and practical agent construction techniques. Developers learning to build context-aware, collaborative AI agents use this course material to gain hands-on experience with real MCP and A2A implementations grounded in industry best practices.
Prerequisites
- Python 3.10 or higher installed
- Access to the LinkedIn Learning course (for video instruction context)
- An OpenAI or compatible LLM API key for running agent examples
- The `mcp` Python library and any other course-specific dependencies
- An MCP-compatible client for testing completed exercises
Clone the course repository
Clone the LinkedIn Learning course repository to get all exercise files and code samples.
git clone https://github.com/LinkedInLearning/hands-on-ai-building-ai-agents-with-model-context-protocol-mcp-and-agent2agent-a2a-6055298.git
cd hands-on-ai-building-ai-agents-with-model-context-protocol-mcp-and-agent2agent-a2a-6055298Review the repository structure
Explore the directory layout to find the exercise files organized by course chapter. Each chapter folder contains starter and finished code.
ls -laInstall Python dependencies
Install the required packages for the course exercises, including the MCP library.
pip install -r requirements.txtSet up your API credentials
Create a .env file with your LLM provider API key. The exercises use environment variables to keep credentials out of source code.
# Create .env and add:
# OPENAI_API_KEY=your-openai-api-keyFollow along with the course chapters
Work through each chapter's exercises in sequence. Start with MCP server basics, then move to client integration, and finally explore Agent2Agent communication patterns.
Test your MCP server with the Inspector
Use the MCP Inspector tool to interactively test the MCP servers you build during the course.
npx @modelcontextprotocol/inspectorHands-On AI Agents MCP Examples
Client configuration
Sample MCP client configuration for a course exercise server built during the MCP chapter.
{
"mcpServers": {
"course-mcp-server": {
"command": "python",
"args": ["server.py"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key"
}
}
}
}Prompts to try
Example prompts for exploring the MCP and A2A concepts covered in the course exercises.
- "What tools are available on this MCP server?"
- "Demonstrate how an agent can call a tool and use the result in its response."
- "Show me an example of two agents communicating using the Agent2Agent protocol."
- "Build a simple MCP server that exposes a weather lookup tool."Troubleshooting Hands-On AI Agents MCP
ImportError when running course exercise files
Ensure you have installed the dependencies with `pip install -r requirements.txt` and that you are using the correct Python version (3.10+). Consider using a virtual environment to avoid conflicts.
MCP Inspector cannot connect to a course exercise server
Ensure the server is running in the correct directory and that the transport type in the Inspector is set to STDIO. Check that no previous instance of the server is still running on the same port.
API key errors when running agent examples
Confirm your .env file is in the same directory as the script you are running and that python-dotenv is installed. Alternatively, export the key directly in your shell: `export OPENAI_API_KEY=your-key`.
Frequently Asked Questions about Hands-On AI Agents MCP
What is Hands-On AI Agents MCP?
Hands-On AI Agents MCP is a Model Context Protocol (MCP) server that this repo is for linkedin learning course: hands-on ai: building ai agents with model context protocol (mcp) and agent2agent (a2a) It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Hands-On AI Agents MCP?
Follow the installation instructions on the Hands-On AI Agents MCP GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Hands-On AI Agents MCP?
Hands-On AI Agents MCP works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Hands-On AI Agents MCP free to use?
Yes, Hands-On AI Agents MCP is open source and available under the NOASSERTION license. You can use it freely in both personal and commercial projects.
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