#Cognitive Debiasing MCP Servers

AI agents accept premises without questioning, analyze from one perspective, cherry-pick evidence, ignore consequences, and present uncertainty as certainty. This tool forces rigor: surface assumptions, apply competing frameworks, weigh counterevidence, trace ripple effects, bound confidence.

LLMs reason by statistical proximity, confusing word co-occurrence with direct causal relationships. This tool forces causal graph isolation: identify entities as nodes, map directed influence edges, isolate statistical associations, validate graph coherence, and derive paths strictly from the DAG.

AI models recite memorized answers to classic puzzles, failing when variables or rules are changed. This tool forces cognitive decontamination: isolate variables, compare prompt rules against standard puzzle templates, execute first-principles logic step-by-step, and prove decontaminated output.

LLMs present fabricated information as fact. This tool forces epistemic rigor: cite verifiable sources for every claim, quantify confidence per assertion, separate facts from opinions, declare knowledge boundaries, and cross-reference for internal contradictions.