This project is a deep, production-oriented GenAI system build that demonstrates how modern companies enable natural language access to operational databases using agentic architectures. The core objective is to design and implement a MongoDB-backed MCP (Model Context Protocol) Server that exposes database operations as discoverable, reusable tools, allowing AI agents to safely and intelligently interact with live data.
You will start by understanding and implementing the MCP lifecycle—initialization, capability discovery, tool invocation, and connection closure—focusing on why MCP introduces stateful, decoupled communication between agents and systems. You will design a MongoDB DB client abstraction layer that handles connection management, querying, inserts, updates, aggregations, and error handling, following real-world software engineering best practices.
On top of this data layer, you will build modular MCP tools (query, insert, update, aggregation, analytics, chart generation), each designed with clear contracts and descriptions so that an agent can autonomously decide which tool to invoke and when. The project highlights why tools must be loosely coupled, independently deployable, and scalable across teams—mirroring enterprise development workflows.
Next, you will construct an agent using LangGraph, enabling structured decision-making, multi-step reasoning, and controlled tool execution. You will see how an LLM alone cannot access databases, and how agent-tool orchestration bridges this gap, converting natural language questions into executable database operations without hallucination.
Finally, the agent will be exposed as a FastAPI service, showing how GenAI agents are deployed behind APIs in production. This includes request handling, tool routing, response formatting, and clean system boundaries. The project also compares Agent-based tool execution vs RAG, clarifying when each approach is appropriate in real systems.
By the end of this project, you will not just “build an agent,” but understand why this architecture exists, how companies design AI-native data platforms, and how to confidently explain, extend, and defend such systems in senior-level interviews.