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Related Servers

Alternatives to Lakeflow MCP Server

No user-submitted related servers found.

    Related Servers

    • A
      license
      Not graded
      quality
      Not graded
      maintenance
      Enables LLMs to manage Databricks clusters, jobs, and notebooks while providing schema references for gold and silver data layers. It allows agents to perform data discovery and execute SQL queries directly against Databricks environments.
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables AI assistants to interact with Databricks workspaces programmatically, providing comprehensive tools for cluster management, notebook operations, job orchestration, Unity Catalog data governance, user management, permissions control, and FinOps cost analytics.
      252 npm
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.
      1
      GPL 3.0
    • F
      license
      Not graded
      quality
      D
      maintenance
      Enables AI assistants like Claude to interact with Databricks workspaces through custom prompts and tools. Supports running SQL queries, managing clusters, creating jobs, and accessing workspace resources via the Databricks SDK.
      2
      -

    TDQS

    A3.6/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: building a wheel, creating a job, listing job runs, triggering a run, and uploading a wheel. The descriptions specify unique actions on different resources (wheel files vs. Databricks jobs), making misselection unlikely.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (e.g., build_wheel, create_job, list_job_runs, trigger_run, upload_wheel). The verbs are clear and descriptive, and there are no deviations in naming style across the set.

    Tool Count5/5

    With 5 tools, this server is well-scoped for its purpose of managing Databricks job workflows with Python wheels. Each tool earns its place by covering distinct steps in the process, from building and uploading wheels to job creation and execution.

    Completeness4/5

    The tool set provides strong coverage for core workflows: building, uploading, job creation, triggering runs, and monitoring runs. Minor gaps exist, such as no tools for updating or deleting jobs, but agents can likely work around this for basic operations.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues