Expense Tracker MCP Server

# 💰 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 | Component | Description | | ------------------------- | -------------------------------------- | | **FastMCP** | Exposes database operations as tools | | **LangChain MCP Adapter** | Connects LLM to MCP tools | | **Ollama (Llama 3.2:3b)** | Interprets user intent and calls tools | | **SQLite** | Persistent expense storage | --- ## 📂 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. ```json { "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) ```bash ollama pull llama3.2:3b ``` ## 📦 Install Dependencies ```bash uv add fastmcp langchain langchain-mcp-adapters langchain-ollama ``` ## ▶️ Running the Client Update paths inside `client.py`: ```python "command": "/home/omkar/.local/bin/uv", "args": [ "run", "fastmcp", "run", "/full/path/to/main.py" ] ``` Then run: ```bash 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 👍