AI agents call get_run_status 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 status information about a job run, which is a read-only query operation with no side effects. It does not create, modify, delete, or execute any operations—it only fetches and returns data about an existing run's state. The low severity reflects minimal blast radius even if misused by an AI agent, as it cannot cause data loss, execute code, or affect system state.
From the tool's definition Tool name 'get_run_status' and description 'Get status for a job run' indicate a retrieval operation that queries the state of an existing job execution without modifying or executing anything.
Documented attack patterns abuse exactly the kind of access get_run_status 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_run_status:
{
"version": "1",
"default": "deny",
"tools": {
"get_run_status": {}
}
}get_run_status is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get status for 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_run_status: 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_run_status 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_run_status 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_run_status. 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_run_status is provided by the Databricks MCP Server MCP server (markov-kernel/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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38 Databricks MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.