databricks_publish_dashboard

A write tool on the Databricks MCP server.

SERVERDatabricks MCP Server SOURCEpypi:databricks-sdk-mcp

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/io-github-pramodbhatofficial-databricks-sdk-mcp/databricks-publish-dashboard.md

WHAT IT DOES

What databricks_publish_dashboard does on Databricks MCP Server

AI agents use databricks_publish_dashboard 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 databricks_publish_dashboard is rated Medium

The description is empty, so classification relies solely on the tool name. 'Publish' typically means making a dashboard visible or live, which is a write/update operation. It is unlikely to be destructive or financial. Confidence is lowered due to lack of description.

From the tool's definition Tool name: 'databricks_publish_dashboard' — the word 'publish' implies creating or making a resource publicly/widely available, which is a write-type action.

Attacks that exploit this kind of access

RECOMMENDED RULE

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

databricks_publish_dashboard Rate-limited

databricks_publish_dashboard 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": {
    "databricks_publish_dashboard": {
      "limits": [
        {
          "counter": "databricks_publish_dashboard_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 databricks_publish_dashboard call is checked against it from then on.

FAQ

Questions about databricks_publish_dashboard

What does the databricks_publish_dashboard tool do? +

databricks_publish_dashboard is a write tool on the Databricks MCP Server MCP server. 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 databricks_publish_dashboard? +

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

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

Can I rate-limit databricks_publish_dashboard? +

Yes. Add a rate_limit block to the databricks_publish_dashboard 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_publish_dashboard completely? +

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

databricks_publish_dashboard 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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