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Define, validate, and visualize semantic metrics with trust scoring and BI integrations.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "semantic-metrics-modeling-assistant" yet — see the docs or source repo.
Define three semantic metrics for an e-commerce platform: GMV, net revenue, and repeat purchase rate. Include definitions, dimensions, filters, refresh cadence, dependent tables, and potential ambiguities with standardization recommendations.
A structured metric specification with business definitions, data dependencies, and naming standardization guidance.
Check whether the current semantic layer definitions for active users, paying users, and conversion rate conflict. Identify duplicate metrics, incompatible dimensions, inconsistent filters, and provide trust scores.
A validation report listing conflicts, risk levels, trust scores, and recommended fixes.
Based on the existing semantic metrics model, generate an implementation plan for BI dashboards, including visualization field mappings, common analysis views, permission considerations, and a launch checklist.
A BI integration plan covering field mappings, recommended charts, access controls, and rollout steps.
Enable AI to safely analyze business metrics without improvising raw SQL.
Discover, manage, and execute tools across MCP servers with natural language.
Track AI usage, costs, logs, and debug model interactions across apps.
Let AI agents store, search, and recall memories that improve over time.
Let AI agents analyze corpora through MCP for discovery, search, and data access.
Connect to Fabric and Power BI semantic models for browsing, querying, and optimization.