LLMs are great at writing market commentary. The problem is they can sound confident even when they haven't looked at any data. That’s fine for casual chat, but it’s not fine if you’re building a feat

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A step-by-step guide to building a personal financial assistant using the Model Context Protocol (MCP) with EODHD's MCP server for market data. The key design pattern is the 'narrator' approach: Python deterministically fetches prices and fundamentals via MCP tool calls, computes metrics (returns, volatility, max drawdown, trend slope, regime labels), and only then passes a structured facts object to an LLM for narrative generation. This prevents hallucinated numbers. The guide covers building an MCP client wrapper, request parsing, metric computation, watchlist correlation/ranking utilities, and an orchestration function. Two demos are shown: a single-ticker market brief for AAPL and a multi-ticker watchlist snapshot comparing TSLA, NVDA, and AMZN. The structured response includes the narrative, raw computed metrics, tool call audit trail, and a trace ID.

How to Use the Model Context Protocol to Build a Personal Financial Assistant

What is MCP, and How Does it Change the Integration Story?

What Makes this Shippable, and What Can Be Improved?