Query Fabric KPIs, finance, and project intelligence in natural language.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "FabricIQ MCP Server" yet — see the docs or source repo.
Use FabricIQ MCP Server to query the current project's KPIs and summarize the main changes in plain language.
Returns KPI query results and a concise plain-language summary.
Use FabricIQ MCP Server to query financial data related to this project and turn it into a readable explanation.
Outputs the relevant financial data with an explanation.
Ask FabricIQ MCP Server for quantified project intelligence such as KPIs, finance, and other measurable metrics.
Summarizes the key points of quantified project intelligence.
A data analyst can query project KPIs and financial metrics directly, then turn them into quick insights. It fits anyone who wants Fabric data through natural language.
A product manager can retrieve project metrics and financial information in natural language and package them for reporting. It helps turn query results into stakeholder-friendly updates.
It is a server that exposes a Microsoft Fabric Data Agent as a native MCP tool. Users can query KPIs, financial data, and other quantified project intelligence in natural language.
According to the description, it supports KPIs, financial data, and other quantified project intelligence. Other capabilities are not specified in the provided information.
The provided information does not include installation or prerequisite details; see the source repository for more.
Connect to Microsoft Fabric for data work, integrations, and automation.
Read Fabric semantic models and run DAX queries on Power BI datasets.
Query Microsoft Fabric workspaces, lakehouses, tables, jobs, and dependencies in natural language.
Connect AI workflows to Microsoft Fabric warehouses and SQL analytics endpoints.
Connect Fabric AI with MCP apps to use patterns and models in IDEs.
Connect to Fabric and Power BI semantic models for browsing, querying, and optimization.