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Verified Access MCP Server

MCP Server generated by mcp.ag2.ai

Installation

Add the following to your MCP client configuration file.

Configuration

{
  "mcpServers": {
    "ag2-mcp-servers-chrome-verified-access-api": {
      "command": "python",
      "args": [
        "mcp_server/main.py",
        "stdio"
      ],
      "env": {
        "CONFIG": "JSON_STRING_WITH_CONFIG",
        "SECURITY": "YOUR_SECURITY_VARS",
        "CONFIG_PATH": "PATH_TO_CONFIG_JSON"
      }
    }
  }
}

You run an MCP Server that exposes a context-enabled API for the Google Verified Access service. It lets you connect an MCP client to a local Python process that serves standardized, context-rich calls, enabling efficient development, testing, and integration with client tooling.

How to use

Start the server in stdio mode to run as a local process that you can interact with from your development environment. The server accepts a single command line argument and runs as a Python-based MCP endpoint. Use a client to connect to the server and perform model-context interactions, testing, and data retrieval via the MCP protocol.

How to install

Prerequisites You need Python 3.9 or later. You also need pip and uv to install and run dependencies.

Step 1: Install Python dependencies Install the editable package with development extras.

pip install -e ".[dev]"

If you prefer using uv to run the installation workflow, you can use:

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

Step 2: Run the server in stdio mode Use the exact command shown here to start the MCP server locally.

python mcp_server/main.py stdio

Additional configuration and notes

The server can be configured via environment variables. Use these to supply a configuration path, a JSON string with the configuration, or security parameters.

Environment variables you can use include:

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 (for example, API keys).

Available tools

linting

Code quality checks performed with ruff for linting and formatting to ensure consistent style.

static analysis

Static analysis using mypy, bandit, and semgrep to catch type issues, security concerns, and code smells.

tests

Test suite executed with pytest to validate behavior and provide coverage reports.

pre-commit

Pre-commit hooks enforce formatting, linting, and basic static checks before commits.