5-Day-AI-Agents-Intensive-Course-with-Google

This MCP server represents a repository of course notes, codelabs, and related resources for the 5-Day AI Agents Intensive Course with Google. While the repository itself is primarily educational content rather than a runnable MCP service, MCP-style tooling can be used to organize, query, and orchestrate long-running actions around the material (for example, executing codelabs in sequence, retrieving whitepapers, or coordinating tool demonstrations). Users can leverage the MCP concepts (Models, Tools, Orchestration, Memory, Evaluation) to design workflows that fetch course resources, run associated notebooks, and evaluate agent outputs. The included materials reference the Model Context Protocol (MCP) as a framework for interoperability between tools and agents, which you can reproduce in your own MCP-enabled environment to experiment with agent-tools interactions and long-running operations.

Prerequisites:

• Python 3.9+ installed on your system
• Basic familiarity with command line and virtual environments

Step-by-step installation (assuming you will run an MCP-enabled server bottle around this content):

1. Create and activate a Python virtual environment

   • On macOS/Linux: python3 -m venv venv source venv/bin/activate
   • On Windows: python -m venv venv venv\Scripts\activate

2. Upgrade pip and install MCP-related tooling (example: a hypothetical MCP framework package) pip install --upgrade pip pip install mcp-framework # replace with the actual MCP framework/package you intend to use

3. If you plan to run a local MCP server module (as suggested in the mcp_config), ensure you have the server module available

   • If using a placeholder module name: ensure mcp_server module is present in your PYTHONPATH

4. Verify installation python -m mcp_server --help # or the actual startup command you configure

5. Run the server (example) python -m mcp_server

Note: This repository is focused on course content rather than distributing a ready-to-run MCP server. Adapt the installation steps to the actual MCP framework and server module you deploy.

Tips and considerations:

• The repository centers on Google’s 5-Day AI Agents course content, including MCP concepts. If you implement an MCP server, map the course elements (Models, Tools, Orchestration, Memory, Evaluation) to modular components for reuse across codelabs and notebooks.
• Common MCP-related issues include version mismatches between the MCP framework and Python dependencies, and ensuring long-running tasks have proper memory/state management if you implement Sessions and Memory.
• Environment variables in mcp_config can be used to describe or supply placeholders for resource names, descriptions, and defaults. Update these as you wire the server into your tooling environment.
• If you’re not actually deploying a server, you can still use MCP principles locally by simulating orchestration of notebooks, whitepapers, and Kaggle codelabs through scripted workflows.
• The npm_package field is null since this repository is not an Node.js-based MCP server.