Verdonz MCP
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- AlicenseAqualityBmaintenanceEnables agents to query governed metrics through MCP, including describing metrics, running deterministic SQL against a warehouse, explaining join path preferences, and searching semantically ambiguous terms.8MIT
- FlicenseNot gradedqualityCmaintenanceEnables AI applications to discover and connect to Power BI Desktop semantic models, inspect tables, columns, and measures, and validate or execute DAX queries through MCP.-
- FlicenseNot gradedqualityAmaintenanceEnables AI agents to query databases correctly by serving governed context over MCP, including canonical metric definitions, relationships, business meaning, and guardrails, so answers come back with resolved definitions, fired filters, freshness, and a trust score instead of guesses.3-
- FlicenseNot gradedqualityBmaintenanceEnables authorized AI agents to inspect, query, create, modify, validate, refresh, and manage eligible cloud semantic models in Microsoft Fabric and Power BI through stateless Streamable HTTP.-
- FlicenseNot gradedqualityCmaintenanceEnables an AI application to connect to Power BI Desktop semantic models, discover their tables, columns, measures, and metadata, and validate and execute DAX queries so users can ask analytical questions in natural language and receive explained results.-
- FlicenseNot gradedqualityCmaintenanceEnables AI clients to discover datasets and author dashboards in Dashboard Builder through natural language, while preserving per-user permissions and audit trails via a secure API-key gated gateway.-
TDQS
Scored across 6 tools
list_metrics/get_metric and list_datasets are clearly distinct, and get_evidence is a well-separated retrieval role. verdonz_ask and verdonz_investigate are the closest pair, but the descriptions (business question vs. root-cause investigation) give enough signal to separate them.
All tools share the verdonz_ prefix and use a consistent verb-first style (list_, get_, ask, investigate). Minor deviation: ask and investigate omit the noun object that the other four include, but the pattern remains readable and predictable.
Six tools is well-scoped for a governed semantic-data Q&A surface, with each tool earning its place (two listers, two getters, one ask, one investigate). Nothing feels padded or missing at this granularity.
Covers the core read lifecycle: discover datasets and metrics, ask questions, investigate metric changes, and pull evidence/lineage. Minor gaps around deeper lineage traversal or metric comparison exist, but agents can work around them via ask/investigate.