Moving Beyond LLM Hallucinations in Technical Analysis via Deterministic MCP Tools

Large Language Models are notoriously bad at arithmetic. When you ask a model to interpret complex financial oscillators, it isn't performing calculus; it is predicting the most likely next token based on training data. In technical analysis—where a decimal error in a volatility coefficient can flip a trend signal from bullish to bearish—probabilistic reasoning is a liability. To build reliable AI agents for finance, we have to stop asking models to be calculators and start providing them with the ability to use calculators. This shift moves the intelligence from stochastic estimation to deter