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jupyter-kernel-mcp

jupyter-kernel-mcp

by codewithcheese
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

execute_code

Run Python code in a persistent Jupyter kernel, preserving variables and imports across executions.

Instructions

Execute Python code in a persistent Jupyter kernel with maintained 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": {
      "code": "<code>",
      "kernel_id": "<kernel_id>"
    },
    "name": "execute_code"
  }
}

Input Schema

NameRequiredDescriptionDefault
codeYesPython code to execute (variables persist between executions)
kernel_idYesID of the kernel to execute code in (use list_kernels to see available kernels)
timeoutNoMaximum execution time in seconds

Output Schema

NameRequiredDescriptionDefault
successYes
outputYes
resultYes
errorYes
timestampYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4/5.0
Behavior4/5

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

The description adds the key behavioral trait of 'maintained state' beyond annotations, indicating that variables persist between executions. Annotations already indicate the tool is not read-only, and the description complements them well.

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, well-structured sentence front-loading the core purpose without any wasted 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?

Given the presence of annotations and an output schema, the description is sufficiently complete. It captures the essential point of code execution with state persistence, though it omits potential side effects or error handling details.

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 description coverage is 100%, so the description does not need to add parameter details. It provides no additional information beyond what is already in the schema.

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 tool executes Python code in a persistent kernel with maintained state, distinguishing it from sibling tools focused on kernel lifecycle management.

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 this tool versus alternatives; usage is implied by the context of sibling tools but not stated outright.

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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