Skip to main content
Glama
ChrisChoTW

databricks-mcp

by ChrisChoTW
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

Related Servers

Alternatives to databricks-mcp

No user-submitted related servers found.

    Related Servers

    • 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.
      -

    • sqldb-mcp-server

      akrym1582
      A
      license
      A
      quality
      A
      maintenance
      A read-only MCP server that exposes SQL database access to LLMs, supporting multiple database types, compact columnar results, pagination, and file export.
      7
      8 npm
      MIT

    • bricks-use

      aymenfurter
      A
      license
      Not graded
      quality
      D
      maintenance
      MCP server for executing Databricks SQL queries and comparing table data, supporting CLI and VS Code integration.
      1
      MIT

    • dagsmith

      tmcwilliam707
      A
      license
      Not graded
      quality
      C
      maintenance
      MCP server providing read-only Snowflake metadata tools (schemas, tables, queries, lineage) for agentic data pipeline generation, enabling natural-language-to-pipeline workflows with dbt, Airflow, and Great Expectations.
      1
      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

    C2.8/5.0

    Scored across 25 tools

    Disambiguation4/5

    Most tools have distinct purposes targeting specific Databricks resources (clusters, jobs, tables, etc.), but some overlap exists. For example, get_table_detail, get_table_schema, and get_table_history all focus on table metadata with potentially unclear boundaries for an agent. However, descriptions help differentiate them by specifying different aspects (detail vs. schema vs. history).

    Naming Consistency5/5

    Tool names follow a highly consistent verb_noun pattern throughout, with clear and predictable conventions. All tools use either 'get_', 'list_', or 'search_' prefixes followed by the resource name (e.g., get_cluster_events, list_jobs, search_tables), making them easily readable and systematic.

    Tool Count3/5

    With 25 tools, the count is borderline high for a single server, potentially overwhelming for an agent. While Databricks is a complex platform, this many tools might indicate fragmentation or redundancy, such as multiple table-related tools that could be consolidated. It feels heavy but not extreme.

    Completeness4/5

    The tool set provides broad coverage for monitoring and querying Databricks resources, including clusters, jobs, tables, pipelines, and more. Minor gaps exist, such as lack of create/update/delete operations for many resources (e.g., no create_cluster or delete_job), but agents can work around this with the provided query tool for some operations. Core read and list functionalities are well-covered.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources