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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use AI Model ROI Engine with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Stop guessing. Start building with data-backed model choices.

Included with plan

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Complete set · 4 capabilities

The complete AI Model ROI Engine capability set.

These are the exact actions your AI can choose when you ask it to work with AI Model ROI Engine.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through AI Model ROI Engine.

  1. 01

    Recommend optimal model

    Identifies the best model candidate based on a set of provided options and strict user constraints

  2. 02

    Analyze model switching

    Determines if migrating from a current model to a new candidate is economically beneficial

  3. 03

    Calculate cost performance tradeoff

    Quantifies the relationship between spending more money to gain higher accuracy or lower latency

  4. 04

    Predict maintenance impact

    Adjusts the long-term cost projections based on the frequency of model updates and maintenance needs

Observed, not estimated

852ms average. Fast in production.

AI Model ROI Engine is checked daily against the live service.

Daily averagePeak 966ms
Aug 27Today
Fastest day
729ms
Slowest day
966ms
14-day trend
Improving-20%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 4 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of AI Model ROI Engine, so you can see the experience inside your AI.

It does not authenticate your account with AI Model ROI Engine. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

AI Model ROI Engine Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_P2JDx8XSXJThXWTdwTkMVQ0G0Agz0zxK0z3txYus/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — AI Model ROI Engine capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "ai-model-selection-roi-engine-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_P2JDx8XSXJThXWTdwTkMVQ0G0Agz0zxK0z3txYus/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Who it's for

Built for the work AI Model ROI Engine owners hand off.

This MCP is essential for anyone responsible for the financial and technical architecture of an AI product. If you're an AI Engineer, Product Manager, or ML Ops specialist, you'll use this to move model selection from guesswork to quantitative analysis.

  • 01

    AI Engineer

    Uses it to test model viability against strict latency, cost, and accuracy requirements.

  • 02

    Product Manager

    Uses it to build a business case, showing stakeholders the ROI of a specific model choice.

  • 03

    ML Ops Specialist

    Uses it to plan model upgrades and calculate the true cost of switching infrastructure.

FAQ

Questions AI Model ROI Engine owners ask.

  • 01

    Does this MCP only look at inference cost?

    No. It goes beyond just the per-token cost. It includes the total cost of ownership, factoring in model switching costs and annual maintenance projections.

  • 02

    Can I use this to compare different LLM providers?

    Yes. You provide the specs for multiple models, and the MCP compares their performance and cost profiles against your specific business requirements.

  • 03

    What if I don't know which model to use?

    You can start by defining your non-negotiable constraints (e.g., 'must be under $0.01/1k tokens' and 'must be >90% accurate'). The MCP will narrow down the best options for you.

  • 04

    Is this useful for predicting future costs?

    Absolutely. The predict_maintenance_impact capability specifically adjusts long-term cost projections based on how often the model requires updates or patches.

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