MCP For Claude Marketplace
RAG Economics Analyzer

RAG Economics Analyzer MCP Connector for Claude

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

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

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.

ragllmcost-analysisinfrastructureai-economics

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

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

analyze_latency_impact

Calculates the additional cost required to meet a specific latency target

See how to talk to your AI agent using RAG Economics Analyzer.

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?

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

How much will it cost to reduce my latency from 500ms to 200ms?

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

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

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

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