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kernel_start

Destructive

Starts a new Jupyter kernel from scratch, optionally specifying the Python interpreter path to enforce a specific environment or guarantee a clean slate.

Instructions

Shut down any existing kernel for this notebook and start a completely fresh one. Unlike kernel_restart, this always creates a new kernel process from scratch — even if the Python interpreter is unchanged. Use this at session start to enforce a specific Python environment, or to guarantee a clean slate.

python_path: which Python to use for the new kernel.

  • "" or omitted: uses the server's own Python (sys.executable)

  • Absolute path: e.g. "/home/user/project/.venv/bin/python"

  • Name on PATH: e.g. "python3.11" Not supported when connected to a remote server (use remote_connect to select the server; the remote server controls the interpreter).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
python_pathNo
Behavior4/5

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

Annotations already mark destructiveHint=true, but description adds value by explaining the fresh start and the python_path restriction with remote servers, which annotations do not cover.

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?

Well-structured: main action, comparison, use cases, then parameter details. Each sentence serves a purpose with no waste.

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?

Covers main action, differentiation, use cases, and a constraint. Missing explanation of the 'name' parameter, but overall adequate for a destructive tool with no output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so description must explain parameters. It details python_path well but fails to explain the 'name' parameter, leaving a significant gap in parameter understanding.

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 it shuts down existing kernel and starts a fresh one. It explicitly differentiates from sibling kernel_restart by noting it always creates a new process from scratch.

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

Usage Guidelines5/5

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

Provides explicit when to use ('at session start', 'to guarantee a clean slate') and when not ('not supported when connected to a remote server'), with alternative suggested (use remote_connect).

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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