A
licenseB
qualityA
maintenanceEnables AI agents to interact with Microsoft Fabric Real-Time Intelligence services, allowing for seamless data querying, analysis, and streaming capabilities.
39
130
MIT
ms-fabric-mcp-server
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TDQS
B3.4/5.0
Scored across 37 tools
Disambiguation3/5
There is significant overlap between pipeline activity tools (add_copy_activity_to_pipeline, add_notebook_activity_to_pipeline, add_dataflow_activity_to_pipeline, add_activity_to_pipeline) where the generic tool can perform the same functions as the specific ones, causing potential confusion. However, descriptions help clarify the distinctions, and other tools like list_items vs list_workspaces or get_job_status vs get_job_status_by_url have clear boundaries.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (e.g., add_activity_to_pipeline, delete_activity_from_pipeline, list_items, get_job_status). There are minor deviations like livy_run_statement (prefix instead of verb_noun) and execute_dax_query (verb_noun but with a different verb style), but overall the naming is predictable and readable.
Tool Count2/5
With 37 tools, the count is excessive for a single server, making it heavy and potentially overwhelming for agents. While the domain (Microsoft Fabric) is broad, the toolset includes many specialized tools (e.g., multiple Livy session management tools) that could be consolidated or scoped more narrowly.
Completeness4/5
The toolset provides comprehensive coverage for Fabric operations, including CRUD for pipelines, notebooks, semantic models, and jobs, with lifecycle management (create, update, delete, execute, monitor). Minor gaps exist, such as limited update operations for items beyond pipelines (e.g., no update_notebook), but agents can work around these with existing tools.
Maintenance
ActivityMaintained
ResponsivenessUnresponsive