databricks_assign_metastore
A write tool on the Databricks MCP server.
This record as markdown: /tools/io-github-pramodbhatofficial-databricks-sdk-mcp/databricks-assign-metastore.md
What databricks_assign_metastore does on Databricks MCP Server
AI agents use databricks_assign_metastore to create or update resources in Databricks MCP Server, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Databricks MCP Server environment.
Why databricks_assign_metastore is rated Medium
Assignment operations typically modify resource metadata and ownership configurations, which are reversible changes characteristic of Write category. The high severity reflects that metastore assignment in Databricks controls access to organizational data catalogs—misconfiguration could isolate critical data or grant unintended access.
From the tool's definition Tool name 'databricks_assign_metastore' indicates assignment/modification of metastore configuration. The 'assign' verb suggests modifying resource ownership or allocation rather than reading or deletion. No description provided, limiting precision.
Attacks that exploit this kind of access
The rule that runs databricks_assign_metastore 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_assign_metastore, this is the rule to start with:
databricks_assign_metastore stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. 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_assign_metastore call is checked against it from then on.
Questions about databricks_assign_metastore
databricks_assign_metastore is a write tool on the Databricks MCP Server MCP server. It is categorised as a Write tool in the Databricks MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for databricks_assign_metastore: 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_assign_metastore is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the databricks_assign_metastore 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_assign_metastore. 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_assign_metastore 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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