jupyter_stop_kernel · Multi-Tool MCP Server MCP

WHAT IT DOES

What jupyter_stop_kernel does on Multi-Tool MCP Server

AI agents invoke jupyter_stop_kernel to trigger actions in Multi-Tool MCP Server. 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.

WHY IT NEEDS A POLICY

Why jupyter_stop_kernel needs a policy

Stopping a Jupyter kernel is an Execute action because it runs/triggers an external operation whose consequences depend on runtime arguments (which kernel to stop). While not destructive in the sense of permanently deleting data, it abruptly terminates potentially important computations, can cause loss of unsaved work, and disrupts dependent processes.

From the tool's definition Tool name and description: 'Stop/shutdown a running Jupyter kernel' — this triggers an external operation (terminating a kernel process) whose effects depend on which kernel is targeted and what computation it was performing.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

jupyter_stop_kernel Rate-limited

jupyter_stop_kernel 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "jupyter_stop_kernel": {
      "limits": [
        {
          "counter": "jupyter_stop_kernel_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Multi-Tool MCP Server, so you can run agents against it safely.

FAQ

Questions about jupyter_stop_kernel

What does the jupyter_stop_kernel tool do? +

Stop/shutdown a running Jupyter kernel. It is categorised as a Execute tool in the Multi-Tool MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on jupyter_stop_kernel? +

Register the Multi-Tool MCP Server MCP server in PolicyLayer and add a rule for jupyter_stop_kernel: 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 Multi-Tool MCP Server. Nothing to install.

What risk level is jupyter_stop_kernel? +

jupyter_stop_kernel is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit jupyter_stop_kernel? +

Yes. Add a rate_limit block to the jupyter_stop_kernel 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.

How do I block jupyter_stop_kernel completely? +

Set action: deny in the PolicyLayer policy for jupyter_stop_kernel. 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.

What MCP server provides jupyter_stop_kernel? +

jupyter_stop_kernel is provided by the Multi-Tool MCP Server MCP server (shawn-falconbury/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.