databricks_get_vector_search_endpoint
Get detailed information about a vector search endpoint.
This record as markdown: /tools/io-github-pramodbhatofficial-databricks-sdk-mcp/databricks-get-vector-search-endpoint.md
What databricks_get_vector_search_endpoint does on Databricks MCP Server
AI agents call databricks_get_vector_search_endpoint 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_get_vector_search_endpoint is rated Low
This tool retrieves and queries configuration or status data about an existing vector search endpoint. It performs no write, execution, deletion, or financial operations. The action is a straightforward read-only data retrieval with no side effects or blast radius if misused by an agent.
From the tool's definition Tool name contains 'get' and description states 'Get detailed information about a vector search endpoint' — retrieves endpoint metadata without modification.
Attacks that exploit this kind of access
The rule that runs databricks_get_vector_search_endpoint 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_get_vector_search_endpoint, this is the rule to start with:
databricks_get_vector_search_endpoint 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_get_vector_search_endpoint call is checked against it from then on.
Questions about databricks_get_vector_search_endpoint
Get detailed information about a vector search endpoint. 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_get_vector_search_endpoint: 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_get_vector_search_endpoint 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_get_vector_search_endpoint 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_get_vector_search_endpoint. 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_get_vector_search_endpoint 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.
More on Databricks MCP Server, and thousands of servers like it.
This server
Across the catalogue