What are the prerequisites to run this project?
Python 3.11, `uv` (Python package manager), Node.js and npm/npx (to run the Google Maps MCP Server), and API keys for Google Maps Platform, OpenAI, and CometML.
@luuisotorres
Basic information
AI Agent built with Google ADK that leverages Google Maps MCP Server to answer real-world location questions with tool usage and traceable execution via Opik.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-ai-agent": {
"command": "uv",
"args": [
"venv",
"#",
"Create",
"a",
"virtual",
"environment",
"(e.g.,",
".venv)"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
This project demonstrates how to build an AI agent using the Google Agent Development Kit (ADK), OpenAI’s GPT‑4o model, the Google Maps MCP Server, and Opik for observability with CometML. It is designed for developers who want to learn agent development principles and see the Model Context Protocol (MCP) applied in practice.
Clone the repository, create a virtual environment with uv, install dependencies, and copy .env.example to .env with your API keys. Then run uv run adk web to start the ADK web UI at http://127.0.0.1:8000, where you can interact with the agent. The Google Maps MCP Server is started automatically via npx when needed.
Python 3.11, `uv` (Python package manager), Node.js and npm/npx (to run the Google Maps MCP Server), and API keys for Google Maps Platform, OpenAI, and CometML.
The ADK’s `MCPToolset` is configured to automatically start the server via `npx` when the agent needs to use its tools, so no manual startup is required.
The agent can use: `maps_directions`, `maps_geocode`, `maps_reverse_geocode`, `maps_elevation`, `maps_search_places`, `maps_place_details`, and `maps_distance_matrix`.
Traces are sent to CometML using the Opik observability library, which provides a rich UI to inspect LLM calls, tool usage, and overall agent behavior.
The agent uses OpenAI’s GPT‑4o model, accessed through the LiteLLM integration within the Google ADK.
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