The world of AI is moving fast, but LLMs still face a major hurdle: they are often "trapped" behind a knowledge cutoff, unable to interact with our real-time, local, or private data. Enter the Model Context Protocol (MCP).
In this post, I’ll walk through how I used MCP to build a conversational financial advisor that analyzes spending habits and provides real-time budget reports.
What is MCP?
The Model Context Protocol is an open standard that allows developers to create a secure, reliable bridge between AI models (like Claude) and external data sources or tools. Instead of manually feeding spreadsheets into a chat, MCP allows the model to "reach out" and grab the data it needs via specific tools.
The Application: Your AI Financial Partner
My project focuses on turning a standard LLM into a financial expert. By building a custom MCP server in Python, I gave Claude the ability to "see" and "think" about financial data through three core tools:
get_transactions
This tool acts as the data pipeline. While I used simulated data for this project, the logic is built to fetch a list of recent financial activities, including dates, vendors, and amounts.categorize_expense
One of the hardest parts of budgeting is organization. This tool uses logic to sort transactions into buckets like Housing, Food, Entertainment, or Utilities.generate_budget_report
The "heavy lifter." This tool aggregates all categorized data to provide a high-level summary. It calculates total spend vs. budget and identifies areas where the user is overspending.
The Workflow: From Data to Advice
The magic happens when you connect these tools to a conversational interface. Here’s how the interaction looks:
The Query: I ask Claude, "How did I spend my money this month?"
The Tool Call: Claude realizes it doesn't have that info. It automatically triggers get_transactions and categorize_expense.
The Analysis: Claude processes the output and identifies that I've spent 20% more on coffee than last month.
The Advice: Claude provides a natural language response: "You've been hitting the cafes a bit hard! If you cut back two trips a week, you'll stay under budget for the month."
Why This Matters
This isn't just about budgeting. This project demonstrates a shift toward Agentic Workflows. By using MCP, we are moving away from "chatting with a bot" and toward "collaborating with an assistant" that has the agency to use professional-grade tools to solve complex problems.
Reflections
Building this application showed me that the future of AI isn't just bigger models—it's better connectivity. Whether it's managing a bank account or automating an email classification system (like the n8n workflow I built alongside this!), the goal is to make AI a functional part of our daily operations.
Check out my walkthrough video below!