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jupyter-kernel-mcp

jupyter-kernel-mcp

by codewithcheese
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_kernel_status

Read-onlyIdempotent

Retrieve comprehensive status details for a specified Jupyter kernel, including execution state and resource usage.

Instructions

Get detailed status information about a specific Jupyter kernel.

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": "get_kernel_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
kernel_idYesID of the kernel to check status for

Output Schema

NameRequiredDescriptionDefault
kernel_idYes
statusYes
created_atYes
languageYes
env_pathYes
detailsNo

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 declare readOnlyHint=true and idempotentHint=true, covering the safety profile. The description adds 'detailed status' but does not elaborate on what status includes or any behavioral traits beyond the annotations. It is adequate but not enriched.

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

Conciseness4/5

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

The description is a single, concise sentence that front-loads the action and resource. It is appropriately sized for the tool's simplicity, though a bit more detail could be added without harming conciseness.

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 the tool's low complexity (1 parameter, no enums), the presence of an output schema, and annotations covering read-only and idempotent behavior, the description provides sufficient context for an AI agent to understand the tool's purpose and use.

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 coverage is 100%, so the input schema fully describes the parameter. The tool description does not add any additional semantic value beyond what the schema provides. Baseline 3 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 clearly states the verb 'Get' and resource 'detailed status information about a specific Jupyter kernel'. It effectively distinguishes from siblings like list_kernels (listing all) and reset_kernel/stop_kernel (mutations). The slight vagueness of 'detailed status' prevents a 5.

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 its use for checking status of a single kernel, but does not explicitly state when to use it versus alternatives like list_kernels (for overview) or execute_code (for running code). No exclusions or additional guidance are provided.

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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