databricks_command_status
A read tool on the Databricks MCP server.
This record as markdown: /tools/io-github-pramodbhatofficial-databricks-sdk-mcp/databricks-command-status.md
What databricks_command_status does on Databricks MCP Server
AI agents call databricks_command_status to retrieve information from Databricks MCP Server without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why databricks_command_status is rated Low
Status checks are intrinsically non-destructive read operations that query current state without side effects. No description is available, which lowers confidence slightly, but the naming convention strongly suggests this retrieves status information about existing commands rather than executing or modifying them.
From the tool's definition Tool name 'databricks_command_status' contains 'status', implying it queries or retrieves the status of a command without modifying state. The 'command' prefix combined with 'status' suggests a retrieval operation typical of Read category tools.
Attacks that exploit this kind of access
The rule that runs databricks_command_status safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Databricks MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For databricks_command_status, this is the rule to start with:
databricks_command_status is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Databricks MCP Server, apply this rule, and every databricks_command_status call is checked against it from then on.
Questions about databricks_command_status
databricks_command_status is a read tool on the Databricks MCP Server MCP server. 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 databricks_command_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.
databricks_command_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 databricks_command_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 databricks_command_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.
databricks_command_status is provided by the Databricks MCP Server MCP server (pypi:databricks-sdk-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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