databricks_list_spark_versions

A read tool on the Databricks MCP server.

SERVERDatabricks MCP Server SOURCEpypi:databricks-sdk-mcp

Low RISK CLASS

Category Read

Parameters 00 required

Recommended Allowedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-pramodbhatofficial-databricks-sdk-mcp/databricks-list-spark-versions.md

WHAT IT DOES

What databricks_list_spark_versions does on Databricks MCP Server

AI agents call databricks_list_spark_versions to retrieve information from Databricks MCP Server without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

RISK

Why databricks_list_spark_versions is rated Low

This tool queries and returns a list of Spark versions available in a Databricks workspace. It is a read-only operation that retrieves information without modifying, executing, or deleting any resources. The empty description prevents higher confidence, but the tool name clearly indicates a safe listing/retrieval function.

From the tool's definition Tool name 'databricks_list_spark_versions' indicates a list operation that retrieves available Spark versions from Databricks. The verb 'list' is a retrieval operation with no side effects.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs databricks_list_spark_versions safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Databricks MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For databricks_list_spark_versions, this is the rule to start with:

databricks_list_spark_versions Allowed

databricks_list_spark_versions is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "databricks_list_spark_versions": {}
  }
}

ALLOW ONLY THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Databricks MCP Server, apply this rule, and every databricks_list_spark_versions call is checked against it from then on.

FAQ

Questions about databricks_list_spark_versions

What does the databricks_list_spark_versions tool do? +

databricks_list_spark_versions is a read tool on the Databricks MCP Server MCP server. It is categorised as a Read tool in the Databricks MCP Server MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on databricks_list_spark_versions? +

Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for databricks_list_spark_versions: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Databricks MCP Server. Nothing to install.

What risk level is databricks_list_spark_versions? +

databricks_list_spark_versions is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit databricks_list_spark_versions? +

Yes. Add a rate_limit block to the databricks_list_spark_versions rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block databricks_list_spark_versions completely? +

Set action: deny in the PolicyLayer policy for databricks_list_spark_versions. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides databricks_list_spark_versions? +

databricks_list_spark_versions is provided by the Databricks MCP Server MCP server (pypi:databricks-sdk-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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