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0x0L

ipykernel-mcp

by 0x0L

kernel_stop

Stop the active IPython kernel and release system resources to prevent memory leaks after execution tasks complete.

Instructions

Stop the running kernel and clean up resources.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

The description adds meaningful context beyond the name by stating that resources are cleaned up, which is a behavioral disclosure not otherwise provided. However, with no annotations present, the description carries the full burden for behavioral transparency. It doesn't mention that stopping is destructive/irreversible to current execution state, or what happens to pending execute calls or unsaved output, which would be valuable for an agent deciding between stop, interrupt, or restart.

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?

The description is a single, tight sentence with zero waste. Every word earns its place — it states the action (stop) and the secondary effect (clean up resources). It's appropriately sized for the simplicity of the tool.

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?

Given this is a zero-parameter tool with an existing output schema, the description conveys the essential purpose effectively. The 'clean up resources' detail adds needed behavioral context. While it could mention post-condition side effects (e.g., session state loss), the combination of a simple operation, output schema, and sibling name context makes this substantially complete for the tool's complexity level.

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

Parameters4/5

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

There are 0 parameters, so the 100% schema coverage baseline applies and there is nothing for the description to clarify. With no parameters to document, the description doesn't need to add parameter semantics, and the baseline of 4 for zero-parameter tools is appropriate.

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

Purpose4/5

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

The description uses a clear verb+resource combination ('Stop the running kernel') and distinguishes the stopping action from siblings like kernel_start, kernel_restart, and kernel_interrupt. It clearly states the resource being acted upon, making the purpose unambiguous even though it doesn't explicitly contrast with sibling tools.

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?

The description implies the tool is used when a running kernel needs to be stopped, which is reasonably clear given the verb 'Stop' in the name. However, it doesn't explicitly state when NOT to use this (e.g., preferring kernel_interrupt for temporary pauses instead of full stop, or kernel_restart for reinitialization scenarios). The sibling differentiation is implicit rather than explicit.

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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