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Jasuni69

Microsoft Fabric MCP Server

by Jasuni69
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Python
Remote

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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    C2.1/5.0

    Scored across 83 tools

    Disambiguation2/5

    Several tools have overlapping purposes, such as create_notebook, create_pyspark_notebook, and create_fabric_notebook, or generate_pyspark_code vs generate_fabric_code. dax_query and analyze_dax_query, as well as table_schema and get_lakehouse_table_schema, also blur boundaries. Multiple empty descriptions like vacuum_delta and set_table make selection even more ambiguous.

    Naming Consistency3/5

    Most tools follow a readable snake_case pattern with a rough verb_noun structure like list_*, get_*, create_*, update_*, and delete_*. However, there are notable deviations such as onelake_ls, pipeline_run/status/logs, table_preview, and describe_history. The naming is generally predictable within subgroups but inconsistent across the full set.

    Tool Count1/5

    With 83 tools, this server is far beyond a manageable scope for agent tool selection and exceeds the 50+ extreme threshold. Even though Microsoft Fabric is a broad platform, consolidating this many operations into one MCP server creates significant cognitive overhead. This would be better split into focused servers by domain.

    Completeness2/5

    The tool surface covers many Fabric areas including workspaces, lakehouses, warehouses, notebooks, semantic models, pipelines, OneLake, SQL, and Graph integrations. However, CRUD coverage is incomplete: warehouses and lakehouses lack update/delete, reports lack create/delete, and pipelines lack list/update/delete. These gaps create dead ends for common lifecycle workflows.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources