MCP For Claude Marketplace
AI Context Window Economics

AI Context Window Economics MCP Connector for Claude

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Analyze the financial impact of context window scaling on LLM inference costs.

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

This MCP server provides specialized tools to model the economic impact of Large Language Model (LLM) inference. It allows users to calculate profit margins across different context window sizes, optimize token pricing to account for quadratic attention overhead, and partition operational costs between compute and KV cache memory requirements. Use calculate_margin_by_size to identify profitable scaling limits, find_optimal_pricing to set ideal rates, allocate_memory_costs to isolate memory overhead, and evaluate_scalability_risk to detect financial risks from growing context sizes.

llmtoken-pricingcontext-windowinference-costkv-cache

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

calculate_margin_by_size

Determines the profit margin for various context window sizes to identify where scaling becomes unprofitable

evaluate_scalability_risk

Identifies if a specific context window size poses a financial risk due to overwhelming attention or memory costs

find_optimal_pricing

Suggests the ideal price per token to maximize total margin given specific cost constraints

allocate_memory_costs

Breaks down the total operational cost into its constituent parts, specifically isolating the memory cost required for the KV cache

See how to talk to your AI agent using AI Context Window Economics.

Calculate the profit margin for a token price of $0.002 and a base compute cost of $0.0005 across context sizes of 4k, 8k, and 32k tokens, with an attention factor of 1.5.

At 4k tokens, the margin per token is $0.0015. At 8k tokens, the margin is $0.0012. At 32k tokens, the margin is $0.0004, indicating significant cost growth due to attention overhead.

What is the optimal price for a 128k context window if my base compute cost is $0.0001 and the attention factor is 2.0, aiming for a minimum margin of $0.0005?

The suggested token price is $0.0015 to maintain the required margin at a 128k context window.

Is a 1M token context window risky if my token price is $0.001 and compute cost is $0.0002 with an attention factor of 5.0?

Yes, the risk level is high because the quadratic attention overhead at 1M tokens causes the cost per token to exceed the $0.001 revenue.

The tools use an attention factor to model the quadratic scaling of computational costs as the context window grows, ensuring margin calculations remain accurate.

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