# RAG Economics Analyzer AI Agent Connect

> Calculate and optimize the total cost of ownership for RAG infrastructures.

## Overview
- **Category:** analytics
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_bW0DihmAVuNSYlddUrnmK4QjLjMyKslDRonEcVzO/ai-agent-connect
- **Tags:** rag, llm, cost-analysis, infrastructure, ai-economics

## Description

This MCP server provides specialized analytical tools to model the economic impact of Retrieval-Augmented Generation (RAG) systems. It allows AI agents to calculate the total cost per query, analyze how latency requirements affect infrastructure spend, and find the optimal chunking strategy to balance retrieval accuracy against LLM token costs. Use `calculate_query_economics` to get a full cost breakdown, `analyze_latency_impact` to estimate upgrades, and `optimize_chunking_strategy` to find the cost-accuracy sweet spot.

## Tools

### analyze_latency_impact
Calculates the additional cost required to meet a specific latency target

### calculate_query_economics
Calculates the total cost and cost breakdown for a single user query

### get_optimization_priorities
Identifies the primary and secondary drivers for cost optimization

### optimize_chunking_strategy
Finds the ideal chunk size to balance retrieval accuracy against LLM token costs

## Prompt Examples

**Prompt:** 
```
What is the total cost per query if my embedding cost is $0.00002 per token and my LLM cost is $0.002 per token?
```

**Response:** 
```
Based on your parameters, the total cost per query is $0.0045, with the LLM inference being the dominant cost component.
```

**Prompt:** 
```
How much will it cost to reduce my latency from 500ms to 200ms?
```

**Response:** 
```
Reducing latency to 200ms will require an estimated 45% increase in infrastructure spend to support higher-tier compute resources.
```

**Prompt:** 
```
What is the best chunk size for a 1,000,000 token document with a target accuracy of 0.85?
```

**Response:** 
```
The optimal chunk size for your requirements is 512 tokens, which balances retrieval precision with LLM context costs.
```

## Frequently Asked Questions

**How does this tool help reduce RAG costs?**
It identifies the primary cost drivers in your pipeline and suggests optimizations like adjusting chunk sizes or selecting more efficient retrieval strategies using `get_optimization_priorities`.

**Can I model the cost of meeting specific latency SLAs?**
Yes, the `analyze_latency_impact` tool calculates the estimated cost increase required to move from your current latency to a faster target latency.

**What metrics are included in the cost breakdown?**
The `calculate_query_economics` tool provides a detailed breakdown including embedding costs, retrieval costs, and LLM inference costs.