databricks_update_metastore
A write tool on the Databricks MCP server.
This record as markdown: /tools/io-github-pramodbhatofficial-databricks-sdk-mcp/databricks-update-metastore.md
What databricks_update_metastore does on Databricks MCP Server
AI agents use databricks_update_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_update_metastore is rated Medium
Updating a metastore modifies configuration and metadata that affects data governance, permissions, and catalog structure across an organization. This is reversible (Write, not Destructive), but has high blast radius if misconfigured by an AI agent. Severity is high rather than critical because metastore updates typically require elevated privileges and don't directly delete data or move money.
From the tool's definition Tool name is 'databricks_update_metastore'; 'update' indicates modification of data. Metastores are central Databricks metadata repositories controlling catalog, schema, and table definitions. The description is empty, limiting certainty.
Attacks that exploit this kind of access
The rule that runs databricks_update_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_update_metastore, this is the rule to start with:
databricks_update_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_update_metastore call is checked against it from then on.
Questions about databricks_update_metastore
databricks_update_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_update_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_update_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_update_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_update_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_update_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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