Google ADK Development Environment
LocalAbout Google ADK Development Environment
Google ADK Development Environment is a VS Code Dev Container setup for developing Google Agent Development Kit applications with Python. It bundles development tools, Google Cloud SDK, and Python packages, with commands for testing and code checks.
Key features
- 01VS Code Dev Container setup
- 02Python 3.13 environment
- 03Google Cloud SDK included
- 04Auto-formatting and pytest setup
- 05Commands for linting and checks
Use cases
- 01Set up a Google ADK development workspace
- 02Run tests and code-quality checks
- 03Develop with included Google Cloud libraries
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEGoogle ADK Development Environment
This project is set up with a development container optimized for Google Agent Development Kit (ADK) development using Python 3.13.
🚀 Quick Start
- Prerequisites: Make sure you have VS Code with the Dev Containers extension installed
- Open in Dev Container: Open this folder in VS Code and click "Reopen in Container" when prompted
- Wait for Setup: The container will build and install all dependencies automatically
- Start Developing: You're ready to build Google ADK applications!
📦 What's Included
Development Environment
- Python 3.13 - Latest Python version
- VS Code Extensions - Python, Pylint, Black, Jupyter, Google Cloud Code, and more
- Google Cloud SDK - Pre-installed and ready to use
- Git & GitHub CLI - Version control tools
Python Packages
- Google Cloud Libraries - Storage, BigQuery, AI Platform, Speech, Vision, etc.
- Machine Learning - TensorFlow, PyTorch, Transformers, LangChain
- Web Frameworks - FastAPI, Flask, with async support
- Development Tools - pytest, ruff, black, mypy, pre-commit
- Data Processing - pandas, numpy, matplotlib, plotly
Features
- Port Forwarding - Common development ports (3000, 5000, 8000, 8080, 9000)
- Custom Aliases - Helpful shortcuts for ADK development
- Auto-formatting - Code formatting on save
- Testing Setup - pytest configuration
- Jupyter Support - For interactive development
🔧 Configuration
Google Cloud Setup
- Copy
.env.exampleto.envand configure your settings - Run
adk-setupto authenticate with Google Cloud - Set your project ID:
gcloud config set project YOUR_PROJECT_ID
Useful Commands
adk-setup- Setup Google Cloud authenticationadk-test- Run testsadk-format- Format code with black and fix with ruffadk-lint- Run ruff linteradk-check- Run full code quality check (ruff + mypy + pytest)
📁 Project Structure
.devcontainer/
├── devcontainer.json # Dev container configuration
├── Dockerfile # Container image definition
└── bashrc # Custom bash configuration
requirements.txt # Python dependencies
.env.example # Environment variables template
🔍 Development Tips
- Package Management: This setup uses
uvfor fast Python package installation - Code Quality: Pre-commit hooks are automatically installed with ruff, black, and mypy
- Authentication: Use
gcloud auth application-default loginfor local development - Environment Variables: Copy
.env.exampleto.envand customize - Testing: Place tests in a
tests/directory and run withadk-test - Linting: Use
adk-lintfor ruff linting oradk-checkfor comprehensive checks
📚 Additional Resources
- Google Agent Development Kit Documentation
- Google Cloud Python Client Libraries
- VS Code Dev Containers
Happy coding! 🎉
Comments
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From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.