AI agents call get_query_results 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 pre-computed results from a query that has already been executed. It performs a read-only operation on existing data without creating, modifying, deleting, or executing any code. The action is analogous to fetching results from a database query that has already run, which has minimal blast radius if misused by an AI agent.
From the tool's definition Tool name 'get_query_results' and description 'Get results of a completed query' indicate retrieval of query output with no modification or execution of new operations.
Documented attack patterns abuse exactly the kind of access get_query_results 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_query_results:
{
"version": "1",
"default": "deny",
"tools": {
"get_query_results": {}
}
}get_query_results is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get results of a completed query. 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_query_results: 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_query_results 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_query_results 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_query_results. 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_query_results 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.