AI agents call get_statement_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 metadata about statement execution progress without side effects. It is a pure read operation that queries existing state information, similar to checking job status or fetching logs. The low severity reflects minimal blast radius—an AI agent cannot misuse this to harm systems, leak sensitive data at scale, or cause operational damage through status queries alone.
From the tool's definition Tool name 'get_statement_status' and description 'Get statement execution status' indicate a retrieval operation that queries the status of an already-submitted statement.
Documented attack patterns abuse exactly the kind of access get_statement_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_statement_status:
{
"version": "1",
"default": "deny",
"tools": {
"get_statement_status": {}
}
}get_statement_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 statement execution status. 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_statement_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_statement_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_statement_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_statement_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_statement_status 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.