MCP For Claude Marketplace
AI Reasoning Cost Engine

AI Reasoning Cost Engine MCP Connector for Claude

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Calculate unit economics and scaling costs for reasoning-heavy AI applications.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides financial intelligence for AI applications utilizing Chain-of-Thought (CoT) architectures. It allows agents to model the hidden costs of reasoning by calculating total transaction costs, analyzing profit margins, and forecasting scaling economics. Use calculate_transaction_cost to account for reasoning overhead, analyze_profitability_margin to evaluate unit economics, and forecast_scaling_economics to predict long-term profitability as volume grows.

reasoningcost-modelingunit-economicschain-of-thoughtscaling

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

forecast_scaling_economics

Predicts how total costs and profit margins will behave as transaction volume increases

calculate_transaction_cost

Determines the total monetary cost of a single AI request including reasoning overhead

evaluate_overhead_impact

Measures the ratio of "thinking" cost to "answering" cost to identify inefficient reasoning chains

analyze_profitability_margin

Evaluates the financial viability of a transaction by comparing cost against revenue

See how to talk to your AI agent using AI Reasoning Cost Engine.

What is the total cost for a request with 10 reasoning steps, a compute factor of 0.05, and 500 average tokens using the Reasoning Tier?

The total cost for this transaction is $0.75, which includes a base cost and a reasoning overhead of $0.50.

If my cost per transaction is $0.10 and I expect 10,000 transactions, what will my total variable cost be?

The total variable cost for 10,000 transactions will be $1,000.00.

Calculate the margin if a transaction costs $0.20 and I charge the user $0.50.

The margin amount is $0.30, resulting in a margin percentage of 60%.

The `calculate_transaction_cost` tool specifically includes reasoning steps and compute per step to capture the 'hidden' token costs generated during the internal reasoning process.

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