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.
MCP Server Orchestration: 3 MCP Servers, one intelligent agent
Connect GitHub, Elon Musk Physics Prover and Linear MCP servers 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 MCP
Turn any internal API into an MCP server. 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 every 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 4,700+ servers whenever you need
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added every week
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 4,700+ MCP servers and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across every tool, in one conversation. That's what this infrastructure 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 every platform.
Contextual Reasoning
Every 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 MCP servers once, and your agent handles the rest. Every time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius MCP catalog 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 MCP Server Orchestration
Which MCP servers 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.
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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 MCP Servers
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 MCP Servers 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 servers used in this workflow
GitHub
GitHub MCP Server manages repositories, tracks issues, and searches code via AI agents. Connect your GitHub account to your preferred AI client and automate core developer workflows—listing repos, getting file contents, or creating new issues—all from a natural conversation. Manage your entire software development lifecycle without leaving your chat window.
Elon Musk Physics Prover
The Elon Musk Physics Prover forces your AI client to run a strict 5-step algorithm—Question, Delete, Simplify, Accelerate, Automate—before validating any major operational strategy or architecture design. It catches common engineering mistakes like bloat, accepting flawed requirements, and premature optimization.
Linear
Linear lets your AI client read, write, and manage issues directly inside Linear—no tab switching needed. You can list all teams, search for specific bugs, create new tasks with defined priorities, or add comments right from your IDE. It gives your agent full control over project metadata, allowing you to check sprint progress, view project scope, and audit issue status using natural conversation.