Deletes the specified index
AI agents call delete_index to permanently remove resources in Azure Ai Search Python Preview — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
This tool irreversibly deletes a search index, which cannot be undone and constitutes a significant data loss event. While not a financial operation, the destructive category is most appropriate as it represents permanent removal of data structures.
From the tool's definition Tool name is 'delete_index' with description 'Deletes the specified index'. The verb 'deletes' and the irreversible nature of removing an entire index (a primary data structure in Azure AI Search containing documents and configurations) clearly indicates…
Attacks that exploit this kind of access
Deletes the specified index. It is categorised as a Destructive tool in the Azure Ai Search Python Preview MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Azure Ai Search Python Preview MCP server in PolicyLayer and add a rule for delete_index: 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 Azure Ai Search Python Preview. Nothing to install.
delete_index is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the delete_index 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 delete_index. 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.
delete_index is provided by the Azure Ai Search Python Preview MCP server (projectacetylcholine/mcp-server-azure-ai-search-python-preview). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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