Move a cell to a new position in the notebook.
AI agents use cell_move to create or update resources in JupyterMCP — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your JupyterMCP environment.
cell_move creates or modifies notebook state reversibly—reordering cells can be undone by moving them back. It does not execute code (cell_execute is separate), delete data (cell_delete is separate), or cause irreversible harm.
From the tool's definition Tool description states 'Move a cell to a new position in the notebook.' This modifies the notebook structure by reordering cells, which is a reversible change.
Attacks that exploit this kind of access
Move a cell to a new position in the notebook. It is categorised as a Write tool in the JupyterMCP MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Jupyter MCP server in PolicyLayer and add a rule for cell_move: 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 JupyterMCP. Nothing to install.
cell_move is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the cell_move 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 cell_move. 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.
cell_move is provided by the Jupyter MCP server (try3d/jupytermcp). 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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