create_cluster

SERVERDatabricks MCP Server SOURCEandresgarciasobrado91/databricks-mcp-server

Medium RISK CLASS

Category Write

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/andresgarciasobrado91-databricks-mcp-server/create-cluster.md

WHAT IT DOES

What create_cluster does on Databricks MCP Server

AI agents use create_cluster to create or update resources in Databricks MCP Server, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Databricks MCP Server environment.

RISK

Why create_cluster is rated Medium

This tool creates new computational resources in Databricks, which is a reversible write operation (clusters can be deleted). However, it has high severity because cluster creation incurs infrastructure costs and consumes resources. It does not delete data irreversibly (Destructive), execute arbitrary code (Execute), or move money directly (Financial). It falls squarely into Write: creates infrastructure resources.

From the tool's definition Tool creates a new Databricks cluster with parameters like cluster_name, spark_version, node_type_id, and num_workers. The description explicitly states 'Create a new Databricks cluster', which is a write operation that modifies infrastructure state.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs create_cluster 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 create_cluster, this is the rule to start with:

create_cluster Rate-limited

create_cluster stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "create_cluster": {
      "limits": [
        {
          "counter": "create_cluster_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT 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 create_cluster call is checked against it from then on.

FAQ

Questions about create_cluster

What does the create_cluster tool do? +

Create a new Databricks cluster with parameters: cluster_name (string, required), spark_version (string, required), node_type_id (string, required), num_workers (integer), autotermination_minutes (integer). It is categorised as a Write tool in the Databricks MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on create_cluster? +

Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for create_cluster: 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 create_cluster? +

create_cluster is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit create_cluster? +

Yes. Add a rate_limit block to the create_cluster 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 create_cluster completely? +

Set action: deny in the PolicyLayer policy for create_cluster. 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 create_cluster? +

create_cluster is provided by the Databricks MCP Server MCP server (andresgarciasobrado91/databricks-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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