Building a Financial Market Scanner with AI and MCP

Tracking financial markets manually across multiple assets and indicators can be overwhelming. By combining modern AI models with the Model Context Protocol (MCP), you can build an automated system that retrieves real-time data, identifies patterns, and produces structured market summaries. This text-based course guides you through designing and implementing your own financial market scanner. You will understand how protocol-driven tool integration works, how to connect market data APIs to AI reasoning engines, and how to configure automated research workflows. What you'll learn: - Understand core concepts of the Model Context Protocol (MCP) and how it enables AI tools to interact with live data - Configure MCP servers and clients to securely fetch quotes, historical charts, and financial indicators - Design structured prompts and prompts templates tailored for financial analysis and risk evaluation - Build automated workflows that screen stocks, indices, or crypto assets based on custom quantitative criteria - Apply retrieval patterns and structured outputs to generate concise daily market briefs The material begins with fundamental terminology and architecture overviews before progressing into hands-on configuration steps and written code walkthroughs. You will practice connecting data endpoints and orchestrating multi-step analysis tasks entirely through clear text explanations. This course is designed for beginners in financial engineering, developers curious about AI tooling, and market enthusiasts looking to automate their analysis. No prior AI architecture background is required. Start reading today to construct your own automated market scanning pipeline.