Wiring an LLM Agent to Live Crypto Market Data over MCP

The article dives into the challenges of using large language models (LLMs) for real-time crypto trading, emphasizing that relying solely on historical data like candles can be misleading. It explains how a more sophisticated approach, using a keyless self-describing MCP schema, offers a better microstructure for understanding market dynamics. This method is more reliable than traditional REST parsing, ensuring the agent can adapt to sudden market shifts, thus providing more accurate insights. This shift is crucial for developing more effective trading-adjacent AI systems.