MCP AI Agent

MCP AI Agent with Google ADK, Google Maps, and Opik is a fully‑featured example of how to build an autonomous AI agent that leverages the Model Context Protocol (MCP) to access external services, while providing rich observability through Opik and Comet.ml. At its core, the project solves a common pain point for developers: how to give an LLM the ability to query real‑world data (like maps or location services) and reliably trace every step of its reasoning without writing custom adapters from scratch. By combining the Google Agent Development Kit (ADK) with a Google Maps MCP server, the agent can ask for directions, calculate distances, or retrieve place information in natural language. The MCP layer standardizes the request/response format so that any LLM—here GPT‑4o—is able to invoke these tools seamlessly, while the ADK framework supplies memory management, tool orchestration, and a convenient web UI for debugging.

The server exposes a set of MCP endpoints that implement common geospatial queries. When the agent receives a user prompt such as “Give me directions from my office to the nearest coffee shop,” it constructs an MCP tool invocation that the Maps server understands, receives a structured JSON response (coordinates, route steps, estimated travel time), and feeds that back into the LLM’s next turn. Because the MCP contract is language‑agnostic, other tools (weather APIs, calendar services, etc.) could be added with minimal effort. The result is an agent that can reason about real‑world constraints and produce actionable outputs without hard‑coding API calls into the LLM prompt.

Key features of this MCP server include:

• Toolset integration – The agent can call Google Maps for directions, place searches, and distance calculations via a single MCP endpoint.
• LLM‑agnostic orchestration – Built on the ADK, it supports multiple LLM providers; switching from GPT‑4o to Anthropic or Cohere only requires changing a configuration flag.
• Observability with Opik & Comet.ml – Every LLM call, tool invocation, and conversation event is automatically traced. Developers can inspect the full interaction graph in Comet.ml’s UI, enabling rapid debugging and performance tuning.
• Web UI for live interaction – The ADK web interface lets users chat with the agent, see tool calls in real time, and replay past conversations.
• Extensibility – The MCP server can be expanded with additional services (e.g., weather, calendar) without modifying the agent code; new tools are added by defining new MCP endpoints.

Real‑world use cases abound. A logistics company can deploy the agent to plan delivery routes on demand, a travel app can offer dynamic itineraries based on current traffic, and a customer support bot could guide users to the nearest service center. In any scenario where an LLM needs authoritative, up‑to‑date data from external APIs, this MCP server provides a clean, standardized bridge.

Integration into existing AI workflows is straightforward: developers expose the MCP server as an endpoint, configure the ADK agent to use that endpoint for its “maps” tool, and optionally enable Opik tracing. The agent then behaves like a first‑class citizen in any LLM‑driven pipeline, consuming context from the MCP server and emitting structured results that downstream systems can consume. This combination of protocol‑driven tool access, powerful orchestration, and end‑to‑end observability makes the MCP AI Agent a standout solution for building robust, explainable AI assistants.