#First Principles MCP Servers

AI gives surface analysis. Restates the question, misses hidden assumptions, uses single-lens thinking. This tool forces multi-model depth: First Principles decomposition, Second-Order cascades (3 levels), Steelmanning (Ideological Turing Test), Inversion, and Premortem risk mapping.

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 reason by analogy, copying industry norms. This engine is a 6-pivot cognitive trap that forces the agent to discard jargon and derive original solutions exclusively from physical, mathematical, or logical axioms.

An AI accepted every constraint as given, added layers of complexity, and automated bloated processes. That is consulting, not engineering. This tool forces the 5-Step Starbase Algorithm: question requirements, delete parts, simplify survivors, accelerate cycle time, automate last.

A decision report said 'it works well.' That is prose, not proof. This tool forces it to formalize into precise rules, derive from first principles, and unify all cases under one framework. No case-by-case exceptions, no special handling.

An AI recommended restructuring 'because the industry leader does it that way.' That is analogy. Not axiom. The foundational claim was never stated: at what volume does the current structure fail? The problem was never decomposed. The conclusion was asserted with 'obviously'. Never derived. The boundaries were never tested: when does this approach cost more than the value it creates? Archimedes derived pi from polygon geometry. Not from Egyptian estimates. This tool forces axiom examination, decomposition, proof derivation, boundary testing, and leverage point identification.