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jupyter-kernel-mcp

jupyter-kernel-mcp

by codewithcheese
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_variables

Read-onlyIdempotent

Retrieve all defined variables from a specified Jupyter kernel to inspect its namespace.

Instructions

List all variables currently defined in the specified kernel's namespace.

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": "list_variables"
  }
}

Input Schema

NameRequiredDescriptionDefault
kernel_idYesID of the kernel to inspect variables from

Output Schema

NameRequiredDescriptionDefault
successYes
variablesYes
countYes
timestampYes
kernel_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint and idempotentHint, which cover safety and repeatability. The description adds no further behavioral details beyond the stated purpose, so it does not significantly enhance transparency beyond what annotations provide.

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 clear sentence with no unnecessary words, front-loaded with the action and resource. It is appropriately concise for the simplicity of the tool.

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

Completeness5/5

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

Given the tool has only one parameter, complete annotations, and an output schema, the description is sufficient. It covers the purpose, scope, and required input, leaving no critical gaps.

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%, and the parameter description in the schema is already clear. The description adds the word 'namespace' but does not provide meaningful additional context or constraints beyond what the schema 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?

The description clearly states the verb 'List', the resource 'variables', and the scope 'currently defined in the specified kernel's namespace'. It distinguishes from sibling tools that perform actions or retrieve status, leaving no ambiguity about the tool's function.

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 usage when the agent needs to inspect variables in a kernel, but it does not explicitly state when to use this tool over alternatives, nor provide any when-not conditions. Given the distinct sibling tools, the lack of explicit guidance is acceptable but limits clarity.

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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