Extract Architecture Principles Using MCP.
Code patterns formalized, universal laws derived, causal forces identified , replace ad-hoc architecture with mathematical proof
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent reads the GitHub repository: a billing engine with 15 country-specific tax handlers as separate switch-case branches. The agent runs `validate_isaac_newton`.
Formalize: tax = base_amount rate(jurisdiction) modifier(category). Three variables, zero branching, infinite countries. Generalize: 'US sales tax is 8.25%' proves the universal law that ALL tax is a product of base, rate, and modifier.
Causal Forces: driving force is regulatory variation; resisting force is single codebase desire. Derive from Axioms: tax is always a percentage of value, modifiers are multiplicative.
No copying Stripe. Unify: one formula handles all countries without new branches. The agent creates a Notion page with the formal derivation, axioms, and implementation guidance showing how to replace 15 switch-cases with 3 lines.
Connector Orchestration: 3 Connectors, one intelligent agent
Connect GitHub, Isaac Newton Prover and Notion Connectors so your AI agent reads codebase patterns, forces architectural decisions through five rigorous proofs (formal rules, universal principles, causal forces, axiomatic derivation, unified abstraction), and generates Notion pages with mathematically proven architecture laws. Teams making decisions based on 'industry best practices' get a derivation engine that proves or disproves every choice from first principles. No copying competitors. One prompt and your agent derives the universal law governing your system.
Github
triggerReads code patterns, API structures, data models, and system boundaries
get_file_contents search_github_code list_user_repositories Isaac Newton Prover
actionForces five-pivot formal proof: rules, universals, causality, axioms, unification
validate_isaac_newton Notion
actionCreates architecture law documentation with formal proofs and unified frameworks
create_page query_database search_pages Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own Connector
Convert any internal API into a Connector. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Connect & Automate
The 3 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Github, Isaac Newton Prover & Notion ready in the catalog right now
- Add more from 5,800+ servers whenever you need
- Connections are secured and compliant by default
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added weekly
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 5,800+ Connectors and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across all tools, in one conversation. That's what this connectivity layer was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across all platforms.
Contextual Reasoning
Each decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your Connectors once, and your agent handles the rest. Each time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius AI Connectors to handle these for you. You describe what you need. It does the rest.
Principal architects who need proof that a proposed design generalizes beyond current requirements
Teams replacing ad-hoc decisions with documented universal laws from first principles
Companies preparing for due diligence who need architecture documentation with mathematical proof
Domain experts building billing or compliance systems who need unified formulas instead of branching logic
Frequently Asked Questions About This Connector Orchestration
Which Connectors do I need for this workflow?
Three: GitHub, Isaac Newton Prover and Notion. Connect all three to your AI client.
Does this work with Claude Desktop, Cursor or Windsurf?
Yes. Any AI client that supports the Model Context Protocol works , Claude Desktop, Cursor, Windsurf, Cline and others.
What does FRAMEWORK_FRAGMENTED mean?
Your system uses per-case branching instead of a unified abstraction. The Prover demands one formula that handles all cases.
Can this work with multi-language codebases?
Yes. The Prover operates on architectural principles, not syntax. It finds universal laws across Go, Node.js, Python, Rust.
What if the Prover rejects my architecture?
That is the point. PATCHWORK_SOLUTION or CAUSALITY_ABSENT means a structural weakness. The Prover tells you exactly which pivot failed.
How is this different from regular architecture docs?
Regular docs describe what. This proves why using formal math, causal forces, and axiomatic derivation.
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How Connectors Auto-Triage Bug Reports
New bugs detected, severity classified, sprint tickets created, team notified , triage your backlog without a standup
MCP Recipe for Code Review Time Analytics
Review bottlenecks detected, unreviewed PRs surfaced, reviewer workload balanced, team velocity measured , fix your code review process with data
MCP Recipe for Faster Incident Response
Endpoints monitored, failures detected, incidents auto-created, root cause traced to the commit , respond to outages before users tweet
Connectors used in this workflow
GitHub
GitHub MCP lets you manage your entire software development lifecycle through a chat interface. You can check the status of a pull request, list open issues, or search for specific code snippets without ever leaving your primary workspace. It gives your AI agent direct access to your repositories, making it easier to audit codebases or update project statuses on the fly.
Isaac Newton Prover
Isaac Newton Prover forces your AI client to stop giving you 'common sense' answers and start providing rigorous, first-principles proofs. It validates complex decisions by checking for five specific reasoning failures, ensuring your agent moves beyond vague descriptions to formal rules, causal forces, and unified abstractions.
Notion
Notion MCP lets your AI agent read, search, and update your Notion workspace. It turns static docs into a live database your agent can query to find project specs, manage tasks, and update team pages instantly. This makes your company knowledge searchable and actionable from any MCP-compatible client.