MCP.so
登录

Google ADK Development Environment MCP Server

@linus-mcmanamey

基本信息

分类

其他

传输方式

stdio

作者

linus-mcmanamey

关于 Google ADK Development Environment MCP Server

MCP (Model Context Protocol) development environment with Google ADK Python framework integration and custom network discovery server

Google ADK Development Environment MCP Server 配置

暂无标准配置

该服务器的 README 中没有可解析的 MCP 配置块,请前往代码仓库查看安装说明。

代码仓库

Google ADK Development Environment MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Google ADK Development Environment?

A development container optimized for building Google Agent Development Kit (ADK) applications using Python 3.13. It bundles VS Code extensions, Google Cloud SDK, and a pre-configured set of Python libraries for cloud, ML, web, and data development.

How to use Google ADK Development Environment?

Open the project folder in VS Code, click “Reopen in Container” when prompted, and wait for the container to build. After setup, run adk-setup to authenticate with Google Cloud, then start developing. Custom aliases like adk-test, adk-format, and adk-lint are available for common tasks.

Key features of Google ADK Development Environment

  • Python 3.13 with VS Code extensions (Python, Jupyter, Google Cloud Code)
  • Pre-installed Google Cloud SDK and Python client libraries
  • Custom aliases for authentication, testing, formatting, and linting
  • Automatic code formatting on save with black and ruff
  • Port forwarding for common development ports (3000, 5000, 8000, 8080, 9000)
  • Jupyter notebook support for interactive development

Use cases of Google ADK Development Environment

  • Building and testing Google Agent Development Kit applications
  • Developing Python services that use Google Cloud (Storage, BigQuery, AI Platform)
  • Machine learning projects with TensorFlow, PyTorch, Transformers, and LangChain
  • Web API development with FastAPI or Flask with async support
  • Data analysis and visualization using pandas, numpy, matplotlib, and plotly

常见问题

1

What prerequisites are needed to use this container?

You need VS Code with the Dev Containers extension installed. The container builds and installs all dependencies automatically.

2

How do I authenticate with Google Cloud inside the container?

Copy `.env.example` to `.env`, run `adk-setup` to authenticate, then set your project ID with `gcloud config set project YOUR_PROJECT_ID`.

3

What Python packages come pre-installed?

Google Cloud libraries (Storage, BigQuery, AI Platform, Speech, Vision), ML libraries (TensorFlow, PyTorch, Transformers, LangChain), web frameworks (FastAPI, Flask), dev tools (pytest, ruff, black, mypy), and data processing (pandas, numpy, matplotlib, plotly).

4

How do I run tests or check code quality?

Place tests in a `tests/` directory and run `adk-test`. For linting use `adk-lint`, for formatting use `adk-format`, and for a full check (ruff + mypy + pytest) use `adk-check`.

5

Where is the project structure defined?

The `.devcontainer/` folder contains the devcontainer.json, Dockerfile, and bashrc configuration. A root-level `requirements.txt` lists Python dependencies and `.env.example` provides environment variable templates.

评论

其他 分类下的更多 MCP 服务器