powerbi-desktop-mcp
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- FlicenseNot gradedqualityAmaintenanceEnables AI assistants to read and modify Power BI Desktop files programmatically, including querying models, creating measures, and managing relationships through natural language.256-
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to interact with Power BI datasets through natural language, allowing users to query data, generate DAX, and get insights without leaving their AI assistant.124MIT
- AlicenseNot gradedqualityCmaintenanceEnables an AI assistant to build Power BI projects end to end: data modeling (tables, DAX measures, relationships, calculation groups, roles, field parameters) against an open Power BI Desktop via TOM/XMLA, and reports (pages, visuals, formatting, bookmarks, themes) as PBIR files in a .pbip project. Every visual is cross-checked against the real model before being written, so generated dashboards don't break on open.MIT
- AlicenseBqualityDmaintenanceEnables AI assistants to interact with Microsoft Fabric and Power BI services through the Model Context Protocol. Users can manage workspaces, execute DAX queries, refresh datasets, and create Fabric notebooks using natural language.610 npm2MIT
- FlicenseNot gradedqualityDmaintenanceEnables controlling Power BI with AI through natural language commands for creating DAX measures, explaining metrics, suggesting KPIs, and organizing models.-
- FlicenseNot gradedqualityDmaintenanceEnables AI assistants to discover and interact with local Power BI Desktop instances, including inspecting table/column schema and executing DAX queries.2-
TDQS
Scored across 33 tools
Tools are largely distinct and deliberately layered: `audit_dashboard` is a documented orchestrator that points to detail tools, and `analyze_relationships` explicitly says to use it over `list_relationships` for design review. A few boundaries remain fuzzy — `list_data_sources` vs `classify_connections`, `get_model_summary` vs the individual `list_*` calls, and the cluster of Power Query tools (`diagnose_power_query`, `analyze_folding_risk`, `find_query_diagnostics`, `read_mashup_traces`, `list_power_query`) — but the descriptions do most of the disambiguating work.
Every name is snake_case, verb-first, and noun-scoped (list_tables, analyze_model_size, check_relationship_integrity, propose_change). Verb choice varies (list/get/find/analyze/check/audit/read/profile/compare/run) but semantically rather than arbitrarily, so the pattern stays predictable. The propose_change/finish_change pair reads as an intentional two-step workflow rather than a naming break.
33 tools is heavy for an agent to navigate, even though each one maps to a real, non-duplicated audit or inspection task. The audit_dashboard aggregator and clear START HERE guidance offset some of the load, but the surface is well past the comfortable 3-15 range for a single server.
Coverage is unusually broad: model metadata, DAX, Power Query/M, folding, RLS, data sources, runtime traces, offline report layout, file scanning, diffing, profiling, and a guarded propose/apply/rollback edit flow. Gaps are mostly acknowledged hard limits — the compressed DataModel/DataMashup in .pbix is never read or edited (so in-place .pbix measure edits are impossible), and the server cannot trigger refreshes or start/stop Query Diagnostics.