get_job

Get information about a specific Databricks job with parameter: job_id (string, required)

SERVERDatabricks MCP Server SOURCEandresgarciasobrado91/databricks-mcp-server

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/andresgarciasobrado91-databricks-mcp-server/get-job.md

WHAT IT DOES

What get_job does on Databricks MCP Server

AI agents call get_job 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 get_job is rated Low

This tool retrieves metadata about a job without modifying, executing, or deleting any data. It is analogous to a read query or fetch operation. The blast radius is minimal—an AI agent can only view job information that already exists. No data is created, modified, or destroyed.

From the tool's definition Tool is named 'get_job' and described as 'Get information about a specific Databricks job' with a job_id parameter. The verb 'get' and the phrase 'Get information' clearly indicate a retrieval operation with no side effects.

Attacks that exploit this kind of access

RECOMMENDED RULE

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

get_job Allowed

get_job 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": {
    "get_job": {}
  }
}

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 get_job call is checked against it from then on.

FAQ

Questions about get_job

What does the get_job tool do? +

Get information about a specific Databricks job with parameter: job_id (string, required). 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 get_job? +

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

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

Can I rate-limit get_job? +

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

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

get_job 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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