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MCP Server generated by mcp.ag2.ai
Configuration
View docs{
"mcpServers": {
"ag2-mcp-servers-cloud-private-catalog": {
"command": "python",
"args": [
"mcp_server/main.py",
"stdio"
],
"env": {
"CONFIG": "{\"transport\":\"stdio\"}",
"SECURITY": "YOUR_API_KEY",
"CONFIG_PATH": "mcp_server/mcp_config.json"
}
}
}
}You run an MCP Server that exposes a programmable context protocol based on a provided OpenAPI specification. This server lets clients query and interact with the Google Cloud Private Catalog data model in a standardized way, enabling consistent integration and automation across environments.
Start the MCP server in stdio mode to enable local, in-process communication with MCP clients. The server reads its configuration from environment variables and can be started directly from your console. Once running, you connect to it using an MCP client in the transport mode you prefer (stdio, sse, or streamable-http) and perform operations that reflect the underlying Cloud Private Catalog data model.
Prerequisites: Python 3.9+; ensure you have pip and the uv tool installed.
1. Clone the project repository.
2. Change into the project directory.
3. Install development dependencies.
4. Run tests or start the server as needed.
Configure the server using environment variables. The following variables are recognized:
- 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 that carry security parameters such as API keys.
If you want to use a pre-defined development environment, you can rely on the project’s development tooling to validate code quality, static analysis, and tests before starting the server.
Build and packaging uses Hatch. You can build and publish your MCP server distribution with Hatch commands.
To build the project:
hatch build
To publish the project:
hatch publish
Lints and formats Python code to ensure consistency and quality.
Performs static type checking to catch type errors early.
Runs unit and integration tests to verify behavior.
Runs a collection of checks before committing changes to ensure code quality.
Builds and publishes the MCP server package.