Skip to main content
Glama
inav

databricks-mcp

by inav
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Related Servers

Alternatives to databricks-mcp

No user-submitted related servers found.

    Related Servers

    • mcp-databricks

      yabur
      F
      license
      Not graded
      quality
      D
      maintenance
      Provides MCP tools to interact with Databricks SQL warehouses, enabling metadata discovery, table reading, and DDL operations.
      -

    • databricks_ops_mcp

      Sameet1308
      F
      license
      Not graded
      quality
      C
      maintenance
      A governed MCP server exposing Databricks operations as tools for jobs orchestration, SQL execution, notebook creation, Unity Catalog governance, lineage, clusters, and DLT pipelines, with safety features like dry-run and audit logging.
      -

    • Databricks MCP Server

      robkisk
      A
      license
      Not graded
      quality
      D
      maintenance
      Enables LLM-powered tools to interact with Databricks clusters, jobs, notebooks, SQL warehouses, and Unity Catalog through the Model Completion Protocol. Provides comprehensive access to Databricks REST API functionality including cluster management, job execution, workspace operations, and data catalog operations.
      MIT

    • Databricks MCP Server

      nainikayakkali
      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.
      534 npm
      MIT
    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

    C2.9/5.0

    Scored across 303 tools

    Disambiguation4/5

    Resource-prefixed names make most tools easy to tell apart, with each domain (clusters, jobs, UC, MLflow, etc.) following clear CRUD patterns. However, overlaps like uc_model vs mlflow_registered_models, workspace_list vs dbfs_list, and whoami vs account_whoami create some ambiguity in a 303-tool surface.

    Naming Consistency4/5

    The dominant pattern is resource_subresource_verb (e.g., clusters_list, uc_table_create, sharing_providers_get), which is highly predictable. Minor deviations such as whoami, auth_config, jobs_run_now, vs_query_index, and clusters_list_node_types prevent a perfect score.

    Tool Count1/5

    303 tools is an extreme mismatch for an agent-facing MCP surface; even though Databricks is a broad platform, no agent can effectively select from this many tools. This far exceeds the 'too many' threshold and significantly harms usability.

    Completeness4/5

    The tool surface is remarkably comprehensive, covering CRUD/lifecycle operations across clusters, jobs, warehouses, SQL, UC, MLflow, serving, sharing, secrets, permissions, SCIM, repos, DBFS, pools, apps, and account management. Minor gaps exist (e.g., account-level SCIM lacks update/patch, account storage configs lack get/update, legacy dashboards are read-only) but they are workarounds.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources