MCP For Claude Marketplace
AI Portfolio Economics Optimizer

AI Portfolio Economics Optimizer MCP Connector for Claude

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Calculate optimal LLM and SLM model mixes to minimize costs while meeting performance requirements.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides advanced tools for optimizing AI model portfolios. It helps developers and architects balance the high reasoning capabilities of Large Language Models (LLMs) with the cost-efficiency of Small Language Models (SLMs). By analyzing use case distributions, routing accuracy, and performance thresholds, you can determine the most economical model mix. Use calculate_optimal_mix to find the ideal ratio of models, get_cost_savings_report to quantify financial benefits against a pure LLM baseline, and generate_tradeoff_matrix to visualize the relationship between cost savings and performance degradation.

llmslmportfoliocost-optimizationai-economics

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

calculate_optimal_mix

Note: capabilities are required for the calculation. Determines the best ratio of LLMs to SLMs to minimize cost while meeting performance requirements

generate_tradeoff_matrix

Visualizes the relationship between cost reduction and performance degradation

get_cost_savings_report

Compares optimized portfolio cost against a pure LLM baseline

validate_portfolio_feasibility

Validates if a proposed LLM/SLM mix meets performance requirements

See how to talk to your AI agent using AI Portfolio Economics Optimizer.

Calculate the optimal model mix for a workload where 70% of tasks are 'summarization_medium' and 30% are 'reasoning_heavy', with a routing accuracy of 0.95.

The optimal mix for your workload requires a 40% LLM and 60% SLM allocation for summarization tasks, and a 95% LLM allocation for reasoning tasks to maintain performance.

What are the potential cost savings if I switch from a pure LLM setup to an optimized portfolio?

By implementing the optimized portfolio, you can achieve a 35% reduction in total query costs compared to using LLMs for all tasks.

Show me the trade-off between cost and performance for my current model distribution.

The trade-off analysis shows that increasing SLM usage by 20% reduces costs by 15% but results in a 2.5% drop in overall system accuracy.

The `calculate_optimal_mix` tool uses the `routingAccuracy` parameter to adjust expected performance, ensuring that even if a query is misrouted to a less capable model, the overall performance remains above your defined threshold.

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