VPC Access

MCP Server generated by mcp.ag2.ai

python

0

GitHub Stars

python

Language

7 months ago

First Indexed

3 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "ag2-mcp-servers-serverless-vpc-access-api": {
      "command": "python",
      "args": [
        "mcp_server/main.py",
        "stdio"
      ],
      "env": {
        "CONFIG": "{...} (JSON string)placeholder",
        "SECURITY": "API_KEYS or other security parameters",
        "CONFIG_PATH": "path/to/mcp_config.json"
      }
    }
  }
}

This MCP Server enables an MCP client to interact with the Google vpcaccess OpenAPI surface by serving as a contextual bridge. It exposes a programmable endpoint that your client can query and respond to, helping you integrate VPC Access functionality into your AI-assisted workflows or automation pipelines.

How to use

You connect to the MCP server from your client using the stdio transport to start a local server process. Run it in stdio mode to have the server communicate with your MCP client through standard input and output streams. You can provide configuration through environment variables or a JSON configuration file to tailor the server behavior to your environment.

How to install

Prerequisites you need before installing component software:

Python 3.9+
pip for Python package management
uv for running local development utilities

Step 1: Clone the repository and navigate into the project folder.

git clone <repository-url>
cd mcp-server

Step 2: Install development dependencies. You can install in editable mode using pip or via uv to run editable installs.

pip install -e ".[dev]"

Or, if you prefer using uv to install editable dependencies:

uv pip install --editable ".[dev]"

Step 3: Run the server in stdio mode. This starts the MCP server and enables you to connect a client over standard input/output streams.

python mcp_server/main.py stdio

Optional: If you want to install and run tests or perform static analysis during development, you can use the provided scripts as part of your workflow.

Additional configuration and notes

The server can be configured through environment variables or a JSON configuration file. The commonly used environment variables are:

CONFIG_PATH: Path to a JSON configuration file (for example, mcp_server/mcp_config.json)
CONFIG: A JSON string containing the configuration
SECURITY: Environment variables for security parameters (e.g., API keys)

If you provide a configuration file, you can point the server to it via CONFIG_PATH, or pass a JSON string via CONFIG. The server reads these values during startup.

Development tooling and quality checks

This project uses linting, static type checking, and tests to maintain code quality.

ruff check
ruff format

Static analysis and pre-commit hooks are available via the provided scripts and configuration.