Deletes the indexer
AI agents call delete_indexer 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.
Deletion is an irreversible operation that cannot be undone without manual reconfiguration or restoration from backups. Removing an indexer breaks automated data ingestion pipelines and search functionality dependent on that indexer. This has no undo mechanism and represents permanent loss of search infrastructure configuration.
From the tool's definition Tool name explicitly uses 'delete_indexer' which removes an indexer configuration. The description states 'Deletes the indexer' confirming irreversible deletion of a configured search indexer resource.
Attacks that exploit this kind of access
Deletes the indexer. 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_indexer: 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_indexer 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_indexer 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_indexer. 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_indexer 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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