jupyter-kernel-mcp

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start_kernel

Initialize a persistent Jupyter kernel for executing code in Python, TypeScript, or JavaScript, using a specified runtime executable path.

Instructions

Start a new Jupyter kernel with persistent state for code execution.

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": {
      "env_path": "<env_path>"
    },
    "name": "start_kernel"
  }
}

Input Schema

Name	Required	Description	Default
`env_path`	Yes	Full path to language runtime executable (e.g., /usr/bin/python3, /usr/bin/node)
`language`	No	Programming language for the kernel (python, typescript, or javascript). Defaults to python for backward compatibility.	python
`kernel_id`	No	Custom kernel identifier (auto-generated if not provided)
`python_env`	No	DEPRECATED: Use env_path instead. Full path to Python executable for backward compatibility.

Output Schema

Name	Required	Description	Default
`kernel_id`	Yes
`success`	Yes
`message`	Yes
`timestamp`	Yes
`error`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0May 28, 2026

TDQS

A3.7/5.0

Behavior3/5

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

Annotations provide that the tool is not read-only, not destructive, and not idempotent. The description adds 'persistent state', which is useful. However, it does not disclose potential side effects like memory consumption or the need to stop kernels.

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?

Single sentence, clear and to the point. No wasted words. Could be slightly improved by mentioning the return value explicitly.

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 an output schema, the description does not need to explain return values. It covers the core functionality, though it could mention that the kernel_id is returned or that the kernel must be started before code execution.

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 description adds no additional meaning beyond what is already in the schema. The parameters are well-documented in the schema itself.

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 ('Start'), the resource ('new Jupyter kernel'), and a key characteristic ('persistent state for code execution'), which distinguishes it from siblings that execute code or manage kernels.

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 or when not to use this tool. It implies usage when a kernel is needed for execution, but does not mention prerequisites (e.g., valid env_path) or conflicts (e.g., duplicate kernel_id).

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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