Verdonz MCP
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- AlicenseAqualityCmaintenanceEnables AI agents and their users to ask business questions in plain language and receive governed metric values, breakdowns, share-of-total figures, and the exact parameterized SQL behind them, drawn from a single semantic model rather than ad-hoc SQL. It also lets clients inspect available metrics and their definitions, and preview optimized queries without executing them.4MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to retrieve governed business metric definitions and compiled SQL through MCP-style tools, so they can query consistently under their own read-only roles without guessing joins or metric logic.MIT
- 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 gradedqualityBmaintenanceEnables 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.-
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.