AI agents call get_job to retrieve information from Databricks MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves job metadata and information without modifying, executing, or deleting anything. It is a straightforward query operation that reads data from the Databricks workspace. The low severity reflects minimal blast radius—an AI agent misusing this tool could only access job details, not modify or execute jobs.
From the tool's definition Tool name 'get_job' and description 'Get detailed information about a specific job' indicate a retrieval operation with no side effects.
Documented attack patterns abuse exactly the kind of access get_job gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Databricks MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for get_job:
{
"version": "1",
"default": "deny",
"tools": {
"get_job": {}
}
}get_job is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get detailed information about a specific job. It is categorised as a Read tool in the Databricks MCP Server MCP Server, which means it retrieves data without modifying state.
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.
get_job is a Read tool with low risk. Read-only tools are generally safe to allow by default.
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.
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.
get_job is provided by the Databricks MCP Server MCP server (pulkitxchadha/awesome-databricks-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Databricks MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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86 Databricks MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.