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use_notebook

Destructive

Activate a Jupyter notebook by connecting to an existing one or creating a new one, enabling subsequent cell operations.

Instructions

Use a notebook and activate it for following cell operations. All cell operations will be performed on the currently activated notebook. Activate new notebook will deactivate the previously activated notebook. Reactivate previously activated notebook using same notebook_name and notebook_path.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoNotebook operation mode: 'connect' to connect to existing and activate it, 'create' to create new and activate itconnect
kernel_idNoSpecific kernel ID to use (will create new if skipped)
notebook_nameYesUnique identifier for the notebook
notebook_pathYesPath to the notebook file, relative to the Jupyter server root (e.g. 'notebook.ipynb')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYesSuccess message with notebook information
Behavior4/5

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

The description explains the destructive behavior (deactivating previous notebook) which aligns with the destructiveHint annotation. It adds context about reactivation and activation effects beyond the annotation.

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 concise with three short sentences that immediately convey the core function and important behavioral notes. No unnecessary 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 tool's simplicity and the presence of an output schema, the description is nearly complete. It could mention a prerequisite like being connected to Jupyter, but that is implied by the sibling tools.

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 parameters are already well-documented. The description adds minimal extra meaning beyond the schema, mentioning reactivation by name/path but not delving into syntax or format.

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's purpose: to activate a notebook for subsequent cell operations. It distinguishes from siblings like list_notebooks, unuse_notebook, and read_notebook by focusing on activation and state management.

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

Usage Guidelines4/5

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

The description implies when to use (before cell operations) and when to re-activate (use same name/path). It does not explicitly state alternatives or when not to use, but the context is clear enough for an AI agent.

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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