AI agents call get_job_run_logs 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 logs from a completed or running job—a read-only query operation. It does not modify data, execute code, delete resources, or involve financial transactions. The blast radius of misuse is low; an attacker could view logs to gather information about job configurations or outcomes, but cannot alter system state or trigger actions through this tool alone.
From the tool's definition Tool name 'get_job_run_logs' and description 'Get logs from a job run' indicate retrieval of existing data with no modification or side effects.
Documented attack patterns abuse exactly the kind of access get_job_run_logs 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_run_logs:
{
"version": "1",
"default": "deny",
"tools": {
"get_job_run_logs": {}
}
}get_job_run_logs is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get logs from a job run. 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_run_logs: 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_run_logs 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_run_logs 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_run_logs. 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_run_logs 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.