Removes a document from the index
AI agents call delete_document 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.
Deleting documents from a search index cannot be undone without restoration from backups or re-indexing. This is a destructive action with potential data loss. While the blast radius depends on which documents are targeted, the irreversible nature and potential impact on search functionality and data integrity warrant 'high' severity. Confidence is high because the function name and description are explicit.
From the tool's definition Tool name is 'delete_document' and description states it 'Removes a document from the index' — this is an irreversible deletion operation.
Attacks that exploit this kind of access
Removes a document from the 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_document: 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_document 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_document 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_document. 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_document 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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