MCP For Claude Marketplace
AI Model Usage Analytics

AI Model Usage Analytics MCP Connector for Claude

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Analyze AI model cost distribution and usage concentration across product features.

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

This MCP server provides deep visibility into how AI model resources are consumed across your product. It allows you to calculate exact cost attribution per feature using get_feature_cost_breakdown, identify high-consumption areas with analyze_usage_concentration, and find cost-saving opportunities via identify_optimization_targets. You can also evaluate model selection effectiveness using get_routing_efficiency_score to ensure the most efficient models are being used for specific tasks.

ai-usagecost-attributionmodel-optimizationfeature-analyticsefficiency

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

identify_optimization_targets

To find features where costs are high but user engagement is low, or where model selection appears inefficient

get_feature_cost_breakdown

To determine the exact monetary expenditure for each individual product feature

get_routing_efficiency_score

To evaluate how effectively the system is selecting models for specific feature tasks

analyze_usage_concentration

To identify which features are the primary drivers of AI model consumption

See how to talk to your AI agent using AI Model Usage Analytics.

Show me the cost breakdown for features 'chat-v1' and 'image-gen-v2'.

The cost breakdown is: 'chat-v1' has a total cost of $450.00 with 1,200 calls, and 'image-gen-v2' has a total cost of $1,200.00 with 300 calls.

Which features are consuming the most AI resources?

The primary drivers of consumption are 'summarizer-pro' (65% of total calls) and 'translator-api' (20% of total calls).

Are there any optimization opportunities for my features?

Yes, 'legacy-parser' is a high-cost feature with low engagement, offering a potential savings of $150.00 per month if switched to a smaller model.

By using `identify_optimization_targets`, you can find features where high costs don't match user engagement, allowing you to switch to more efficient models.

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