Large language model (LLM) agents are increasingly used to support decisions by calling external tools. Existing work on LLM tool-calling assumes these tools are perfectly reliable. However, in many applications, the tools are statistical predictors with non-trivial errors. We introduce PredActBench, a benchmark for evaluating dialogue agents that work with imperfect predictive tools, using education as a testbed. First, we build a benchmark in which LLMs work with noisy predictors to guide users' decisions. Second, we extend two prior metrics for trust calibration, Relative AI-Reliance and Relative Self-Reliance, to multi-turn dialogue. Third, we evaluate 13 LLMs on OULAD and our released PredAct-CS dataset of 60 courses with real final grades and synthetic weekly scores, alongside a human study involving 13 instructors and teaching assistants. We find that when tools are noisy, SOTA models rarely make predictor uncertainty visible to users, with our strongest baseline GPT-5.5 achieving an F1 of 57.2. As a result, users over-rely on incorrect predictions and unsupported claims, showing that task accuracy alone is not enough for AI decision support and agents must communicate when their tools may be wrong. We release PredActBench to support the development of LLM agents that help users calibrate their reliance.

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