Run a specific cell by its index Args: index: The index of the cell to run
AI agents invoke run_cell to trigger actions in JupyterMCP. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool executes arbitrary code within a Jupyter notebook environment. While the user/agent controls which cell to run via the index parameter, the actual effects depend entirely on the code contained in that cell. This makes it an Execute category tool rather than Read or Write.
From the tool's definition Tool description states 'Run a specific cell by its index' - the verb 'run' combined with execution of notebook cells means arbitrary code execution.
Documented attack patterns abuse exactly the kind of access run_cell gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and JupyterMCP, and nothing reaches the server without passing your rules. This is the rule we recommend for run_cell:
{
"version": "1",
"default": "deny",
"tools": {
"run_cell": {
"limits": [
{
"counter": "run_cell_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}run_cell stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
Free to start. No card required.
Run a specific cell by its index Args: index: The index of the cell to run. It is categorised as a Execute tool in the JupyterMCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Jupyter MCP server in PolicyLayer and add a rule for run_cell: 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.
run_cell is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the run_cell 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 run_cell. 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.
run_cell is provided by the Jupyter MCP server (jjsantos01/jupyter-notebook-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from JupyterMCP, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
11 JupyterMCP tools catalogued and risk-classified — across an index of 43,000+ MCP servers.