MCP Employee & Project Assistant

A small but practical project that shows how a Model Context Protocol (MCP) server can expose real business tools to an AI agent, so a user can ask questions in natural language and have the AI fetch live data from a database.

This project demonstrates a clean pattern:

• an MCP server exposes tools for employee and project data
• a Gemini agent decides when to use those tools
• the agent turns tool results into a readable answer

The goal is simple: instead of writing custom code for every question, the AI can use structured tools to answer real business questions.

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Why this project matters

A normal application would need specific integration for each question a user asks.

With MCP:

• the database logic is wrapped in small tools
• the AI sees those tools as capabilities
• the AI can decide which tool to use based on the user request
• the result is a conversational interface on top of real business systems

This is a great example of AI + tools + structured data working together.

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What is MCP?

MCP stands for Model Context Protocol.

In plain English, MCP is a standard way for an AI application to talk to tools and services.

Think of it like this:

• The AI is the decision-maker
• The tools are the hands and eyes
• MCP is the common language they use to work together

Without MCP, an AI model usually only knows what it was trained on. With MCP, it can access real systems such as:

• databases
• APIs
• internal tools
• business functions

In this project, the MCP server exposes functions like:

• get_employee_details
• get_project_details
• get_projects_of_employee
• get_employees_for_project

These are not just random functions. They are business tools that the AI can call when needed.

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Project architecture

The app is split into a few simple pieces:

• MCP server: exposes the tools
• Services: talk to the database
• Client: connects to the MCP server
• Gemini Agent: decides which tool to call and interprets the response
• PostgreSQL: stores employee and project data

High-level flow:

1. A user asks a question in natural language
2. The Gemini agent checks the available MCP tools
3. The agent chooses the best tool to answer the question
4. The MCP server runs the tool against the database
5. The result is returned to the agent
6. The agent turns the result into a friendly answer

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Folder structure

• server.py – MCP server that exposes business tools
• client.py – lightweight MCP client connection layer
• agent.py – Gemini-powered agent that calls MCP tools
• services/
  • employee.py – employee database logic
  • project.py – project and assignment logic
• architecture.md – architecture notes and learning path
• requirements.txt – Python dependencies

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How the app works in simple terms

1. The server exposes tools

The MCP server says: “Here are the actions I can perform.”

Examples:

• get_employee_details(employee_id)