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local-spark-mcp

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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

    A4.4/5.0

    Scored across 8 tools

    Disambiguation5/5

    Each tool has a clearly distinct role: running Python vs SQL, inspecting session state, resetting, and managing lakehouse/table mounting. The potential overlap between mount_table and mount_lakehouse is clearly scoped.

    Naming Consistency4/5

    Most tools follow a verb_noun snake_case pattern (run_code, run_sql, list_lakehouses, mount_table), but session_info is a noun_noun exception. Still consistent style overall.

    Tool Count5/5

    8 tools is well-scoped for a Spark session server, covering execution, SQL, state management, and catalog operations without redundancy.

    Completeness5/5

    The surface covers the full workflow: execute code, query SQL, inspect session, reset state, list and mount data sources. Auto-mounting in run_sql and explicit mounting in mount_* cover the data access lifecycle.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

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