AI agents call list_schemas 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.
The tool performs a read-only operation that queries and returns metadata about schemas. It does not create, modify, delete, execute code, or trigger external operations. It falls clearly into the Read category with low severity since listing schemas has no blast radius beyond information disclosure, which is typically low-risk in a data platform context.
From the tool's definition Tool name is 'list_schemas' and description states 'List schemas in a catalog' — this is a query/retrieval operation with no data modification or side effects.
Documented attack patterns abuse exactly the kind of access list_schemas 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 list_schemas:
{
"version": "1",
"default": "deny",
"tools": {
"list_schemas": {}
}
}list_schemas is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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List schemas in a catalog. 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 list_schemas: 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.
list_schemas 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 list_schemas 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 list_schemas. 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.
list_schemas is provided by the Databricks MCP Server MCP server (markov-kernel/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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38 Databricks MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.