MCP For Claude Marketplace
AI Model Selection ROI Engine

AI Model Selection ROI Engine MCP Connector for Claude

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Calculate and compare the ROI of different AI models based on cost, performance, and maintenance.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides a decision-support engine for AI engineers and product managers to optimize model selection. It calculates the economic impact of choosing different LLMs by analyzing cost-performance trade-offs, switching costs for migrations, and long-term maintenance projections. Use recommend_optimal_model to find the best candidate within your constraints, calculate_cost_performance_tradeoff to visualize accuracy vs. cost, analyze_model_switching to justify migrations, and predict_maintenance_impact to account for annual update costs.

roillmcost-analysismodel-selectionperformance

4 tools expose this connector's capabilities to your AI agent.

recommend_optimal_model

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

analyze_model_switching

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

calculate_cost_performance_tradeoff

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

predict_maintenance_impact

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

See how to talk to your AI agent using AI Model Selection ROI Engine.

Which model should I use if I need at least 85% accuracy and a latency under 500ms?

Based on your constraints, the recommended model is GPT-4o-mini with an expected monthly cost of $45.00 and a latency of 320ms.

Is it worth switching from Model A ($0.01/inf) to Model B ($0.005/inf) if the migration costs $500 and I have 100,000 inferences per month?

Yes, the migration is justified. The payback period is 100 months, and you will see net first-year savings of $100.00.

Show me the cost-performance trade-off for these models: [{name: 'Model X', costPerInference: 0.02, latencyMs: 200, accuracy: 0.90}, {name: 'Model Y', costPerInference: 0.05, latencyMs: 100, accuracy: 0.95}] with 50,000 monthly inferences.

Model X costs $1,000 per month, while Model Y costs $2,500 per month. You pay an additional $1,500 per month to gain 5% more accuracy.

The `recommend_optimal_model` tool filters out any models that do not meet your specified `minAccuracy` threshold before selecting the most cost-effective option.

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