Manage RAG (Retrieval-Augmented Generation) collections and documents. Collections are named containers for documents that are chunked, embedded, and indexed for semantic search. Actions: Collection actions: - "create_collection": Create a new collection - "list_collections": List all collections...
Risk signalsAccepts file system path (filename) · High parameter count (13 properties)
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AI agents may call manage_rag_content to permanently remove or destroy resources in Mcp. Without a policy, an autonomous agent could delete critical data in a loop with no way to undo the damage. PolicyLayer blocks destructive tools by default and requires explicit human approval before enabling them.
Without a policy, an AI agent could call manage_rag_content in a loop, permanently destroying resources in Mcp. There is no undo for destructive operations. PolicyLayer blocks this tool by default and only allows it when a human explicitly approves the action.
Destructive tools permanently remove data. Block by default. Only enable with explicit approval workflows.
{
"version": "1",
"default": "deny",
"hide": [
"manage_rag_content"
]
}See the full Mcp policy for all 47 tools.
These attack patterns abuse exactly the kind of access manage_rag_content gives an agent. Each links to the full case and the policy that stops it:
Other destructive tools across the catalogue. The same approach applies to each: deny by default, or require human approval.
Manage RAG (Retrieval-Augmented Generation) collections and documents. Collections are named containers for documents that are chunked, embedded, and indexed for semantic search. Actions: Collection actions: - "create_collection": Create a new collection - "list_collections": List all collections in an app - "get_collection": Get details for a specific collection (includes document counts by status) - "delete_collection": Permanently delete a collection and all its documents/embeddings Document actions: - "ingest_document": Add a document (raw text or uploaded file) to be chunked, embedded, and indexed - "list_documents": List all documents in a collection with their status - "get_document_status": Check the processing status of a specific document - "delete_document": Permanently delete a document and its chunks/embeddings Parameters by action: create_collection: { app_id, action: "create_collection", name, description?, access_mode?, chunk_size?, chunk_overlap? } list_collections: { app_id, action: "list_collections" } get_collection: { app_id, action: "get_collection", name } delete_collection: { app_id, action: "delete_collection", name } ingest_document: { app_id, collection, action: "ingest_document", text?, storage_object_id?, filename?, metadata? } list_documents: { app_id, collection, action: "list_documents" } get_document_status: { app_id, collection, action: "get_document_status", document_id } delete_document: { app_id, collection, action: "delete_document", document_id } access_mode options (create_collection): - "private" (default): Only the app owner can query - "shared": All authenticated users can query - "custom": Use RLS policies for fine-grained access Ingestion modes for ingest_document (provide one): 1. Raw text: provide "text" directly 2. File-based: upload via manage_storage (action: "upload_url") first, then provide "storage_object_id" Supported file types: PDF, TXT, Markdown, CSV, HTML, DOCX, XLSX, PPTX. Document statuses: "pending" → "processing" → "ready" (or "failed") Workflow: create_collection → ingest_document → poll get_document_status until "ready" → query with rag_query. Warning: "delete_collection" permanently removes the collection, all documents, and embeddings. Cannot be undone. Warning: "delete_document" permanently removes the document and its embeddings. To replace, delete then re-ingest. Common errors: - RESOURCE_NOT_FOUND: App, collection, or document doesn't exist - VALIDATION_DUPLICATE_NAME: Collection name already exists (create_collection) - VALIDATION_ERROR: Neither text nor storage_object_id provided (ingest_document). It is categorised as a Destructive tool in the Mcp MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the MCP server in PolicyLayer and add a rule for manage_rag_content: 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 Mcp. Nothing to install.
manage_rag_content 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 manage_rag_content 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 manage_rag_content. 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.
manage_rag_content is provided by the MCP server (@butterbase/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 47 Mcp tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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