💰 Expense Tracker using MCP (FastMCP + LangChain + Ollama)- Sample Project for understanding MCP

This project demonstrates a simple end-to-end MCP (Model Context Protocol) example where:

• A FastMCP server exposes tools to manage expenses stored in SQLite

• A LangChain client connects to the MCP server

• An LLM (Llama 3.2 via Ollama) decides when to call tools

• Natural language queries like

  "Add my expense 500 to groceries" automatically trigger backend database operations

📌 Architecture Overview

User (CLI)
   │
   ▼
LangChain Client (client.py)
   │
   │  MCP (stdio)
   ▼
FastMCP Server (main.py)
   │
   ▼
SQLite Database (expenses.db)

Key Components

📂 Project Structure

.
├── main.py        # FastMCP expense database server
├── client.py      # LangChain MCP client with LLM
├── expenses.db    # SQLite database (auto-created)
└── README.md

🚀 Features

• ✅ Add expenses using natural language
• ✅ View total expenses
• ✅ List all expenses
• ✅ Automatic tool selection by LLM
• ✅ Persistent storage using SQLite
• ✅ MCP-compliant architecture

🛠️ Tools Exposed by MCP Server

The FastMCP server exposes the following tools:

add_expense

Adds a new expense entry.

{
  "amount": 500,
  "category": "groceries",
  "description": "weekly shopping"
}

get_total

Returns the total sum of all expenses.

get_all_expenses

Returns a list of all recorded expenses.

⚙️ Prerequisites

Make sure you have the following installed:

• Python 3.10+
• Ollama
• Llama 3.2 model
• uv (Python package runner)

ollama pull llama3.2:3b

📦 Install Dependencies

uv add fastmcp langchain langchain-mcp-adapters langchain-ollama

▶️ Running the Client

Update paths inside client.py:

"command": "/home/omkar/.local/bin/uv",
"args": [
    "run",
    "fastmcp",
    "run",
    "/full/path/to/main.py"
]

Then run:

uv run client.py

🧠 How It Works (Step-by-Step)

1. User enters a natural language query

2. LLM decides whether a tool is needed

3. If required:

   • Tool name + arguments are generated

4. LangChain invokes MCP tool

5. Result is returned to LLM

6. LLM generates final user-friendly respons

Just tell me 👍