MCP For Claude Marketplace
AI Explainability Economics

AI Explainability Economics MCP Connector for Claude

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Calculate the economic impact and infrastructure costs of AI explainability features.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides a suite of tools to evaluate the financial viability of implementing AI explainability (XAI). It bridges the gap between technical overhead and business value by calculating infrastructure expenditure, compliance benefits, and customer trust ROI. Use calculate_infrastructure_expenditure to determine hardware and storage costs, estimate_compliance_benefit to quantify risk mitigation, and calculate_trust_roi to measure retention gains. Finally, use generate_economic_summary to assess the net economic impact and project viability.

xaieconomicscomplianceroiinfrastructure

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

calculate_infrastructure_expenditure

Determines the direct additional cost of hardware and storage needed to support XAI

calculate_trust_roi

Quantifies the business value gained from increased user confidence

estimate_compliance_benefit

Calculates the theoretical value of avoiding regulatory fines and legal friction

generate_economic_summary

Provides a consolidated view of the total cost versus the total benefit

See how to talk to your AI agent using AI Explainability Economics.

What is the infrastructure cost for SHAP with 10% compute overhead and 500KB storage per explanation for 1,000,000 inferences?

The total infrastructure cost for this configuration is $1,250.00, consisting of $1,000.00 in additional compute and $250.00 in storage.

Calculate the trust ROI for a customer base where 20% require transparency, with a 5% retention lift and a lifetime value of $5,000.

The expected trust ROI is $5,000.00.

Is it viable to implement an XAI solution with a $5,000 cost, $4,000 compliance benefit, and $2,000 trust ROI?

Yes, the project is viable with a net economic impact of $1,000.00.

You can use the `calculate_infrastructure_expenditure` tool, which takes into account the explanation method, compute overhead, storage per explanation, and total inferences.

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