Scope Containment Prover Connector for AI agents.
1 live capability
Stop AI over-engineering and enforce YAGNI principles on your software projects.
Waiting for input…
Why people use Scope Containment Prover
Scope Containment Prover for Stopping Software Scope Creep
The Scope Containment Prover changes the dynamic by forcing the agent to stop and think. Before it writes a single line of code, it has to pass a 6-point check. It has to tell you what it is not doing. This means your agent becomes a disciplined partner that respects your time and your budget, resulting in lean, shippable code.
What Vinkius changes
That your agent stops over-engineering and starts shipping functional code faster.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Stopping microservices overkill
When the AI tries to build a complex microservices architecture for a simple internal to-do app, this Connector forces it to stick to a single, maintainable service.
- Real-world use case 02
Cutting down feature bloat
When a file upload request starts turning into a media library with folders and search, the agent uses validate_scope_containment to cut back to the core CSV import.
- Real-world use case 03
Justifying new libraries
When you want to add a new package, this Connector forces the agent to prove it can't be solved in 20 lines of native code first.
Complete set · 1capability
The complete Scope Containment Prover capability set.
These are the exact actions your AI can choose when you ask it to work with Scope Containment Prover.
01
1 capability in this set.
Part of 1 available through Scope Containment Prover.
- 01 Capability
Validate scope containment
Forces the agent to justify the core problem, YAGNI requirements, scale, dependencies, maintenance, and MVP status. It provides a structured reflection to catch bloat before any code is written.
Set up in minutes
One URL. Then ask Scope Containment Prover to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Scope Containment Prover from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_xIyXFLpRAGOgmqLP0Tdt7rR41ka6TleZEKIb6bTG/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Scope Containment Prover, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Scope Containment Prover for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_xIyXFLpRAGOgmqLP0Tdt7rR41ka6TleZEKIb6bTG/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Scope Containment Prover URL.
- Step 03
Save and start
Save the connection and enable Scope Containment Prover in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"scope-containment-prover": {
"url": "https://edge.vinkius.com/vk_preview_xIyXFLpRAGOgmqLP0Tdt7rR41ka6TleZEKIb6bTG/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Scope Containment Prover
Open Agent mode in chat and ask: "Using Scope Containment Prover, help me...". 1 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"scope-containment-prover": {
"url": "https://edge.vinkius.com/vk_preview_xIyXFLpRAGOgmqLP0Tdt7rR41ka6TleZEKIb6bTG/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Scope Containment Prover
Ask Copilot: "Using Scope Containment Prover, help me...". 1 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"scope-containment-prover": {
"url": "https://edge.vinkius.com/vk_preview_xIyXFLpRAGOgmqLP0Tdt7rR41ka6TleZEKIb6bTG/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Scope Containment Prover
Open Cascade and ask: "Using Scope Containment Prover, help me...". 1 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"scope-containment-prover": {
"url": "https://edge.vinkius.com/vk_preview_xIyXFLpRAGOgmqLP0Tdt7rR41ka6TleZEKIb6bTG/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Scope Containment Prover
Ask Cline: "Using Scope Containment Prover, help me...". 1 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add scope-containment-prover --transport http "https://edge.vinkius.com/vk_preview_xIyXFLpRAGOgmqLP0Tdt7rR41ka6TleZEKIb6bTG/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Scope Containment Prover
Ask Claude: "Using Scope Containment Prover, show me...". 1 tools are ready
Where the request belongs
Work Scope Containment Prover can move forward.
Software engineers and product managers who are tired of AI-generated code that is too complex to maintain. It's for the person who needs to ship a prototype next week and can't afford a scalable architecture that takes three months to build.
Software Architect
Uses it to vet high-level designs and ensure the system doesn't become a monolith of unnecessary just in case features.
Product Manager
Uses it to keep the development team focused on the core user value and prevent scope creep during the sprint.
Full-stack Developer
Uses it to prevent the AI from pulling in heavy libraries for simple tasks like date formatting or string manipulation.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse Connectors
Systems Thinking Prover
AI thinks in straight lines. This engine is a 6-pivot cognitive trap that forces the LLM to map feedback loops, second-order effects, and bottlenecks before proposing any architectural change.
Inversion Thinking Prover
AI agents are sycophantic. They agree with your bad ideas. This engine forces a 6-pivot cognitive trap: agents must destroy their own hypotheses, define measurable kill criteria, and simulate post-mortem failures before executing code.
First Principles Prover
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.
Einstellung-Challenger Prover
AI models default to complex, familiar heuristics even when simpler solutions exist. This capability breaks suboptimal cognitive sets: identify default heuristics, search for counterexamples, map alternative paths, benchmark complexity metrics, and choose the most elegant solution.
Context Engineering Prover
An AI dumped 80,000 tokens into a prompt. 64,000 of them unreferenced noise. It said 'best practice' to justify the structure and 'looks good' to measure quality. That is not context engineering. that is a copy-paste pipeline. This capability forces five context axes: relevance auditing, priority structuring, token budgeting, evidence grounding, and quality measurement.
Deep Analyst Prover
AI gives surface analysis. restates the question, misses hidden assumptions, uses single-lens thinking. This capability forces multi-model depth: First Principles decomposition, Second-Order cascades (3 levels), Steelmanning (Ideological Turing Test), Inversion, and Premortem risk mapping.
Bring your own AI
Change the model, client or framework. Keep Scope Containment Prover connected.
-
Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Scope Containment Prover.
The practical details behind the request, access and result.
How does the Scope Containment Prover help with my project's scope?
It acts as a gatekeeper that forces your agent to focus on the core problem. It prevents the AI from adding nice-to-have features that bloat your code and delay your launch.
Can I use the Scope Containment Prover to stop my AI from over-engineering?
Yes, that's its primary job. It forces the agent to justify why it's choosing a specific architecture and ensures it's building for your current needs, not hypothetical future ones.
What is YAGNI and how does this Connector use it?
YAGNI stands for You Aren't Gonna Need It. The Connector forces your agent to explicitly list the features it is rejecting to keep the project lean and focused.
Will this help reduce the number of dependencies in my app?
Definitely. The Connector requires the agent to justify every library it wants to add. If it can solve the problem in a few lines of native code, it's forced to do that instead of adding a new package.
How does the Scope Containment Prover handle MVP definitions?
It checks if your proposed MVP can actually be finished in a reasonable timeframe. If the agent's plan looks like it will take months, the Connector flags it as bloated and asks for a smaller scope.
Is the Scope Containment Prover good for early-stage startups?
It's ideal for startups. It helps you stay lean by ensuring you don't spend weeks building features that your first ten users won't actually use.
Does this Connector help with long-term maintenance?
Yes, it forces the agent to calculate the Total Cost of Ownership for every feature, including the burden of testing, documentation, and monitoring.
What does YAGNI mean?
'You Aren't Gonna Need It'. It's the principle of not building features until you actually need them.
Why force rejection of premature optimization?
Because optimizing for 1M users when you have 10 adds massive complexity that slows down development.
Why map maintenance cost?
Code is a liability, not an asset. Every line written must be read, tested, and updated forever.
One connection away
Give your agent a direct line to Scope Containment Prover.
Connect Scope Containment Prover once. Keep it beside 5,900+ managed Connectors when the next task needs more.
Explore every Connector No credit card required · Free tier available