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jupyter-kernel-mcp

jupyter-kernel-mcp

by codewithcheese
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

reset_kernel

DestructiveIdempotent

Clear all variables and state from a Jupyter kernel while keeping the same ID for continued use.

Instructions

Reset a Jupyter kernel by clearing all variables and state while keeping the same ID.

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": "reset_kernel"
  }
}

Input Schema

NameRequiredDescriptionDefault
kernel_idYesID of the kernel to reset (WARNING: This will clear all variables and state)

Output Schema

NameRequiredDescriptionDefault
kernel_idYes
successYes
messageYes
timestampYes
errorNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already mark destructiveHint=true. Description adds that the kernel ID remains same, but doesn't explain side effects like aborting running code or impact on connected clients.

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 key information, no superfluous words.

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

Completeness3/5

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

Output schema exists, so return values are covered. However, for a destructive tool, additional context like 'kernels must be running' or 'state is lost permanently' would improve completeness.

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?

Schema already describes kernel_id and includes a warning. Description adds no further meaning beyond what schema provides. Schema coverage is 100%.

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?

The description clearly states the action (reset), the resource (Jupyter kernel), and the effect (clearing all variables and state while keeping ID). It distinguishes from sibling tools like start_kernel or stop_kernel.

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

Usage Guidelines2/5

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

No guidance on when to use this tool vs alternatives like restart_kernel or similar. No mention of prerequisites or scenarios where reset is appropriate.

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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