jupyter_profile_code · ML Jupyter MCP: Risk & Policy

WHAT IT DOES

What jupyter_profile_code does on ML Jupyter MCP

AI agents invoke jupyter_profile_code to trigger actions in ML Jupyter MCP. 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_profile_code needs a policy

The tool name suggests profiling code, which on a Jupyter-based execution server almost certainly involves running/executing Python code. The description is empty, so confidence is reduced, but given the server context and sibling tools that all execute code, this tool likely runs code to measure its performance. Execute is the appropriate category.

From the tool's definition Tool name 'jupyter_profile_code' on a server described as executing Python code with persistent state; sibling tools include execute_code, jupyter_execute_cell, jupyter_execute_notebook

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

jupyter_profile_code Rate-limited

jupyter_profile_code 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_profile_code": {
      "limits": [
        {
          "counter": "jupyter_profile_code_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 ML Jupyter MCP, so you can run agents against it safely.

FAQ

Questions about jupyter_profile_code

What does the jupyter_profile_code tool do? +

jupyter_profile_code. It is categorised as a Execute tool in the ML Jupyter MCP 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_profile_code? +

Register the ML Jupyter MCP server in PolicyLayer and add a rule for jupyter_profile_code: 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 ML Jupyter MCP. Nothing to install.

What risk level is jupyter_profile_code? +

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

Can I rate-limit jupyter_profile_code? +

Yes. Add a rate_limit block to the jupyter_profile_code 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_profile_code completely? +

Set action: deny in the PolicyLayer policy for jupyter_profile_code. 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_profile_code? +

jupyter_profile_code is provided by the ML Jupyter MCP server (mayank-ketkar-sf/claudejupy). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.