MCP Recipe to Kill Codebase Bloat.
Codebase audited, bloat identified, requirements questioned, lean tickets created , kill architectural complexity before it ships
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: 8 microservices, 3 API gateways, 2 message queues, a service mesh, and a custom orchestration layer.
It reads key files: docker-compose.yml, Kubernetes manifests, service entry points, shared libraries. For each service, the agent runs `validate_elon_musk_physics`. The auth-gateway service? Step 1: WHO required a separate auth gateway? The auth middleware in the API already handles JWT validation.
Step 2: DELETE the auth-gateway , it duplicates functionality. The custom orchestration layer? Step 1: WHO required custom orchestration when Kubernetes already handles it? Step 2: DELETE.
The agent then runs Step 3 on survivors: the payments service has 47 API endpoints , only 12 are called in production.
Simplify to 12. Step 4: the CI pipeline takes 28 minutes. Accelerate by parallelizing test suites. Step 5: only NOW automate the simplified, lean architecture.
The agent creates Linear tickets: 'DELETE: auth-gateway service (duplicates API middleware)' with priority P1, 'SIMPLIFY: payments-api from 47 to 12 endpoints' with full justification, 'ACCELERATE: CI pipeline parallelization target 8 minutes.' Each ticket includes the Starbase Algorithm verdict as evidence.
Connector Orchestration: 3 Connectors, one intelligent agent
Connect GitHub, Elon Musk Physics Prover and Linear Connectors so your AI agent reads your repository structure, runs every proposed service through the 5-Step Starbase Algorithm (Question, Delete, Simplify, Accelerate, Automate), and creates Linear tickets for every piece of bloat that needs deletion. Engineering teams drowning in microservice sprawl, unnecessary abstraction layers, or premature infrastructure get an automated audit that strips complexity to the bone. No architecture review meetings. No subjective opinions. One prompt and your agent questions every requirement, deletes what should not exist, and files actionable cleanup tickets.
Github
triggerReads repository structure, service boundaries, configuration files and dependency graphs
get_file_contents search_github_code list_user_repositories get_repository_details Elon Musk Physics Prover
actionRuns the 5-Step Starbase Algorithm on every architectural decision to identify bloat
validate_elon_musk_physics Linear
actionCreates prioritized cleanup tickets for every component flagged for deletion or simplification
linear_create_issue linear_search_issues linear_get_teams 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, Elon Musk Physics Prover & Linear 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.
Engineering teams with microservice sprawl who need an automated audit to identify which services should be deleted entirely rather than maintained
CTOs preparing for cost reduction who need evidence-based justification for infrastructure simplification with actionable tickets
Platform teams maintaining Kubernetes clusters with unnecessary complexity who need a first-principles review of every component
Startups that over-architected early and need to strip back to the minimum viable infrastructure for their actual scale
Frequently Asked Questions About This Connector Orchestration
Which Connectors do I need for this workflow?
Three: GitHub, Elon Musk Physics Prover and Linear. 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.
Will the agent actually delete code?
No. The agent reads the codebase and creates Linear tickets with deletion recommendations. The engineering team reviews and executes the changes.
How does the Starbase Algorithm differ from a standard code review?
A standard code review checks if code works. The Starbase Algorithm checks if code should exist at all. It forces requirement questioning before any optimization, ensuring you do not optimize waste.
Can I run this on a monorepo with multiple teams?
Yes. The agent processes each service independently and creates team-specific Linear tickets. Each ticket includes the full audit trail so teams understand the reasoning.
How often should I run this audit?
Quarterly or before major architecture decisions. Run it whenever someone proposes adding a new service, infrastructure component, or abstraction layer.
Deploy Containers to Production Using MCP
Code pushed, images built, tags verified, deploys triggered, status reported , ship containers from commit to production in one prompt
Extract Architecture Principles Using MCP
Code patterns formalized, universal laws derived, causal forces identified , replace ad-hoc architecture with mathematical proof
Find Codebase Duplications Using Connectors
Your codebase has 4 different implementations of date formatting, 3 versions of the retry logic, and 2 competing validation libraries , but nobody knows because grep only finds exact matches and these duplicates are semantic
Generate Error Postmortems Automatically via MCP
Errors captured, stack traces analyzed, root cause commits identified, postmortem docs generated , write incident reports without the pain
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
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.
Elon Musk Physics Prover
Elon Musk Physics Prover is an architecture tool that forces your AI to follow the 5-Step Starbase Algorithm. Instead of letting your agent add complexity, it forces it to question requirements, delete parts, simplify survivors, accelerate cycle time, and only then automate. It stops consulting bloat and forces first-principles engineering.
Linear
Linear MCP lets your AI agent manage your project boards, sprints, and issue tracking without you having to switch tabs. It handles everything from creating tickets and assigning priorities to checking cycle progress and querying team data.