Building a Stock Analysis MCP Application from Scratch: A Step-by-Step
Model context protocol (MCP) represents an evolution of traditional paradigms like function calling and AI agents. It introduces a structured, context-aware approach to integrating external tools and resources with AI systems. MCP's goal is to refine and extend these capabilities, enabling AI to interact with the broader digital ecosystem seamlessly.
In this hands-on tutorial, we will build an MCP-based stock analysis server and integrate it with Claude Sonnet, enabling intelligent, context-aware interactions for real-time financial insights.
Table of Contents:
Introduction to MCP
Setting up the Working Environment
Building Stock Analysis MCP Server
Launching MCP Server
Setting Up the MCP Server for Claude Desktop
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1. Introduction to MCP
Model Context Protocol (MCP) is a stateful, context-preserving framework designed to enable intelligent, multi-step interactions between AI agents and users.
Unlike traditional stateless API calls, MCP introduces a persistent context layer, allowing models to retain memory, learn from ongoing interactions, and make autonomous decisions over time.
Imagine an MCP-powered travel assistant that remembers your dietary restrictions, preferred destinations, travel history, and budget, adapting its itinerary recommendations over time without requiring you to repeat information.
Core concepts of MCP:
Statefulness: Maintains both session-level and long-term memory, enabling continuity across interactions.
Interoperability: Designed to work seamlessly across models, tools, environments, and data sources.
Agent-Centric Design: Focuses on empowering autonomous agents to make decisions within defined operational boundaries.
MCP follows a client-server model, allowing integration with LLM-based tools such as Claude Desktop and IDE plugins. Below is a breakdown of its components and communication flow.
Host: The application that initiates and manages AI interactions (e.g., Claude Desktop).
Client: A component embedded in the host that maintains a dedicated connection to an MCP server.
Server: Manages tools, memory context, and request routing for the connected client.
2. Setting up the Working Environment
The first step is to navigate to the Claude.ai website and download the installer for Claude Desktop. Proceed with the installation according to your operating system.
Then, install the MCP and YFinanace Python package using the PIP command.
$ pip install "mcp[cli]" yfinance3. Building Stock Analysis MCP Server
We will begin by creating a new project directory with a name of your choice. Inside this directory, create a Python file named stock_price_server.py.
Add the Following Core Components to stock_price_server.py:
MCP Server Initialization: Initializes a configurable MCP server instance named “Stock Price Server” using the FastMCP class.
Stock Price Retrieval (get_stock_price): Fetches the latest price for a given stock ticker. Includes logic to handle market closure scenarios with fallback options.
Resource Exposure (stock_resource): Formats stock price data into a human-readable string and exposes it as a resource endpoint.
Historical Data Retrieval (get_stock_history): Returns historical stock prices in CSV format for a specified date range, enabling time-series analysis.
Stock Comparison (compare_stocks): Compares current prices of two stock tickers and returns a structured message highlighting their relative values.
Robust Error Handling: All server functions are wrapped with error-handling logic to provide informative messages when data fetching or processing fails.