jupyter_configure_kernels

Configure custom kernels and environments (Pro feature)

SERVERPlugin Hub SOURCE@barnburner121/plugin-hub-mcp

Medium RISK CLASS

Category Write

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/barnburner121-plugin-hub-mcp/jupyter-configure-kernels.md

WHAT IT DOES

What jupyter_configure_kernels does on Plugin Hub

AI agents use jupyter_configure_kernels to create or update resources in Plugin Hub, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Plugin Hub environment.

RISK

Why jupyter_configure_kernels is rated Medium

An AI agent can call jupyter_configure_kernels faster than any human can review: one bad instruction and it creates or modifies resources in Plugin Hub by the hundred, each call as confident as the last.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs jupyter_configure_kernels safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Plugin Hub, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For jupyter_configure_kernels, this is the rule to start with:

jupyter_configure_kernels Rate-limited

jupyter_configure_kernels stays usable, but capped: an agent stuck in a loop can't make hundreds of changes 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_configure_kernels": {
      "limits": [
        {
          "counter": "jupyter_configure_kernels_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Plugin Hub, apply this rule, and every jupyter_configure_kernels call is checked against it from then on.

FAQ

Questions about jupyter_configure_kernels

What does the jupyter_configure_kernels tool do? +

Configure custom kernels and environments (Pro feature). It is categorised as a Write tool in the Plugin Hub MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on jupyter_configure_kernels? +

Register the Plugin Hub MCP server in PolicyLayer and add a rule for jupyter_configure_kernels: 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 Plugin Hub. Nothing to install.

What risk level is jupyter_configure_kernels? +

jupyter_configure_kernels is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit jupyter_configure_kernels? +

Yes. Add a rate_limit block to the jupyter_configure_kernels 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_configure_kernels completely? +

Set action: deny in the PolicyLayer policy for jupyter_configure_kernels. 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_configure_kernels? +

jupyter_configure_kernels is provided by the Plugin Hub MCP server (@barnburner121/plugin-hub-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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