# AI Portfolio Economics Optimizer AI Agent Connect

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

## Overview
- **Category:** optimization
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_XzAEDVOVYaCSnEL7ACyI8wrokOIjK3bIH83SxaU0/ai-agent-connect
- **Tags:** llm, slm, portfolio, cost-optimization, ai-economics

## Description

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.

## Tools

### 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

## Prompt Examples

**Prompt:** 
```
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.
```

**Response:** 
```
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.
```

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

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

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

**Response:** 
```
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.
```

## Frequently Asked Questions

**How does the optimizer account for routing errors?**
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.

**Can I compare my current setup to an optimized one?**
Yes, you can use `get_cost_savings_report` to compare the cost of your optimized portfolio against a baseline where only LLMs are used for every task.

**How do I know if my model mix is actually viable?**
You can use `validate_portfolio_feasibility` to check if a specific proposed allocation of LLMs and SLMs will meet your required performance levels given your current routing accuracy.