Verificate MCP

Deterministic production-readiness gate for AI-built systems. Verifies the invariants that stop a system silently shipping broken: every critical component is PRESENT and LOADS, the import closure resolves (nothing assumed 'already on the box'), all runtime dependencies are declared, and health is a REAL fail-closed check. Returns approve/reject with a fix plan and ISO 27001 / ISO 5055 control evi

The merge gate for ANY AI-written output — code, documentation, reports, emails, configs: returns a binary approve/reject verdict with veto power — e.g. it rejects code calling the nonexistent stripe.Inventory API, an N+1 loop with the latency arithmetic to prove it, or a doc claiming success with no evidence. Deterministic reality gates (mock/placeholder veto, gaming and bypass detection, invente

The gate for PLANS, designs and specs — run BEFORE any code is written, the cheapest place to catch a bad design. Returns the same binary verdict shape as validate_ai_output, with findings on completeness, feasibility, performance and scalability implications, security risks and missing considerations (e.g. it rejects a plan that polls an API every 100ms per client, with the request-volume math).

Advisory deep-dive on existing code — scores and findings, deliberately NO pass/fail verdict, so it never blocks an agent. Surfaces performance hot paths, scalability cliffs, reliability gaps and tech debt with concrete latency/throughput arithmetic (e.g. 'O(n²) dedup: ~4s at 10k items'). Read-only: the code is analyzed, never executed. Use it to understand a validate_ai_output rejection or review

Generate code and gate it in one step: an LLM writes the implementation, then the same protection engine as validate_ai_output vets it — retrying generation when the gate rejects. If every attempt is vetoed you still receive the last attempt, clearly marked validated:false with the gate findings — rejected code is never presented as clean. Generation runs on our infrastructure; nothing executes in