Yakunashi-Safety Gate MCP Connector for Claude
A+LLMs hallucinate confidently when context is missing. This tool enforces epistemic calibration: map required preconditions, audit information sufficiency, detect speculation (yakunashi), and trigger safe folding (Beta-Ori) when data is missing.
AI agents are trained to be helpful, which creates a cognitive failure mode: they frequently invent plausible-sounding answers (confident hallucinations) when critical parameters or facts are missing from their context. Named after the concept of 'yakunashi' (speculative or uncalibrated completion), this safety gate enforces a strict precondition audit, mapping out what is required and triggering a structured retreat (Beta-Ori) rather than guessing.
The Problem Axis: Epistemic Uncalibration
LLM hallucinations on incomplete context stem from three primary causes:
- Helpfulness Bias — The model is optimized to provide answers, making it reluctant to say 'I do not know' or request clarification.
- Precondition Blindness — The model jumps straight to answering without mapping the minimum set of variables needed for a deterministic solution.
- Speculative Drift — The model interpolates missing records with standard patterns, producing highly confident but incorrect statements.
How It Works
Yakunashi-Safety Gate runs your inputs through a 5-Pivot verification path:
- preconditionsMapped — Has the model defined the minimum necessary facts or variables required to answer the question?
- informationSufficiencyMet — Are all of those variables present and clear in the context?
- yakunashiDetected — Is there a risk that the model is speculating or guessing to fill in missing information?
- safeFoldingTriggered — Has the model executed a safe folding response (Beta-Ori) listing exactly what parameters are missing?
- calibrationVerified — Is the confidence level of the output strictly calibrated to the evidence available?
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