Building a Stock Market Analysis Agent with MCP + LangGraph

Lessons Learned: A Beginner-Friendly Deep Dive into Building an MCP + LangGraph Stock Analysis System When people first start building AI applications, they often imagine the hardest part will be “getting the model to think correctly.” In practice, that is usually not the hardest part. The hardest part is everything around the model: wiring tools correctly choosing the right transport handling async execution understanding orchestration frameworks debugging infrastructure errors This article walks through real-world lessons learned while building a stock analysis system using MCP, LangGraph, and Yahoo Finance . Lesson 1: Tool Names Are Exact Contracts Tool names are not friendly labels. They are strict identifiers. @mcp.tool("get_stock_data") def get_stock_data(...): ... The exact name must be used: await session.call_tool("get_stock_data", args) Even small mismatches will fail. Key takeaway: Tool names are exact cont...