jupyter-kernel-mcp

by codewithcheese

Overview Schema Source Related Servers Discussions Admin

Python

Local

stop_kernel

DestructiveIdempotent

Stop a Jupyter kernel and permanently remove it along with all its state.

Instructions

Stop and permanently remove a Jupyter kernel and all its state.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "kernel_id": "<kernel_id>"
    },
    "name": "stop_kernel"
  }
}

Input Schema

Name	Required	Description	Default
`kernel_id`	Yes	ID of the kernel to stop (WARNING: This will permanently remove the kernel and all its state)

Output Schema

Name	Required	Description	Default
`kernel_id`	Yes
`success`	Yes
`message`	Yes
`timestamp`	Yes
`error`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0May 28, 2026

TDQS

A4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark destructiveHint=true. Description adds specificity: 'permanently remove all its state', clarifying scope of destruction. No other behavioral traits disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, front-loaded with verb and object, zero unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple destructive tool with one param and output schema, description is sufficient. Could briefly mention irreversible nature beyond what annotations provide, but current is adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Single parameter kernel_id is thoroughly described in input schema (including warning). Description provides no additional parameter context beyond what schema already offers.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description states exact action: stop and permanently remove a Jupyter kernel and all state. Sibling tool reset_kernel implies a non-permanent reset, so this tool's permanent removal is distinctive.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use vs. reset_kernel or start_kernel. The description implies permanence, but does not state conditions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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