Indexing API

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-indexing-api": {
      "command": "python",
      "args": [
        "mcp_server/main.py",
        "stdio"
      ],
      "env": {
        "CONFIG": "{...json...}",
        "SECURITY": "{...api_key...}",
        "CONFIG_PATH": "/path/to/mcp_config.json"
      }
    }
  }
}

You run an MCP (Model Context Protocol) server that exposes a computed, context-aware API surface for the Google APIs Indexing v3 OpenAPI spec. It lets clients interact with the underlying model context in a standardized way, supporting various transport modes and configurable security. This guide walks you through using, installing, and configuring the server so you can start serving MCP-based requests quickly.

How to use

You start the MCP server in a local process and connect to it using an MCP client. The server can operate in different transport modes to suit your workflow, such as stdio for local development or other transports supported by the runtime. Once running, your client can request context-aware actions that are defined by the underlying Google Indexing API surface, enabling you to simulate, query, or drive operations in a structured MCP fashion.

Typical usage patterns include starting the server in a development environment, pointing your MCP client at the running process, and providing configuration through environment variables or a configuration file. You can toggle security parameters as needed and observe the server’s behavior through its logs and responses.

How to install

Prerequisites you need before installing the MCP server are Python 3.9 or later, and a working Python tooling environment with pip and uv available.

Step-by-step installation flow you can follow to prepare and run the server locally.

# Prerequisites
# Ensure you have Python 3.9+ installed on your system

# Install dependencies (dev scope)
pip install -e ".[dev]"

# Alternatively, use uv to install editable dev dependencies
uv pip install --editable ".[dev]"

Additional setup and run notes

Run the MCP server in stdio mode for local development. This starts the server and connects it to the standard input/output streams, which is convenient for interactive sessions and testing.

python mcp_server/main.py stdio

Configuring the server

Configure how the server loads its settings through environment variables. You can provide a path to a JSON configuration file or a direct JSON string for flexibility.

Environment variables commonly used include the following. Replace placeholders with your actual values as needed.

CONFIG_PATH=/path/to/mcp_config.json
CONFIG='{"transport":"stdio","serverName":"indexing_api"}'
SECURITY="{\"apiKey\": \"YOUR_API_KEY\"}"

Troubleshooting and notes

If you encounter issues starting the server, verify that Python 3.9+ is active in your shell, dependencies are installed, and the entry point script exists at the expected path. Check logs for errors related to transport mode selection, configuration loading, or security parameters.

Security and environment

Protect sensitive data by using secure configuration practices. When providing API keys or other credentials, ensure they are stored in a secure location and accessed only by authorized processes.

Available tools

ruff

Linting and formatting tool used to enforce code quality and style.

mypy

Static type checker used to ensure type correctness across the codebase.

pytest

Testing framework used to run unit and integration tests.

pre-commit

Hooks that run before commits to enforce quality checks.

hatch

Build and publish tool used to build and release the MCP server.