ipynb_delete_cells_batch
AI agents call ipynb_delete_cells_batch to permanently remove resources in Jupyter Editor — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
Deletion of notebook cells is an irreversible operation that destroys data and cannot be undone programmatically. Batch deletion amplifies the blast radius by enabling removal of multiple cells in a single operation, potentially corrupting or destroying significant notebook content. This meets the Destructive category definition.
From the tool's definition Tool name 'ipynb_delete_cells_batch' indicates batch deletion of notebook cells. The 'delete' operation combined with 'batch' suffix and presence of sibling tool 'ipynb_delete_cell' strongly suggests irreversible removal of cell content.
Attacks that exploit this kind of access
ipynb_delete_cells_batch. It is categorised as a Destructive tool in the Jupyter Editor MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Jupyter Editor MCP server in PolicyLayer and add a rule for ipynb_delete_cells_batch: 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 Jupyter Editor. Nothing to install.
ipynb_delete_cells_batch 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 ipynb_delete_cells_batch 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 ipynb_delete_cells_batch. 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.
ipynb_delete_cells_batch is provided by the Jupyter Editor MCP server (jsamuel1/jupyter-editor-mcp). 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.
Teams ship this data inside their own products. See what a licence covers →