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start_session_resume_notebook

Resume a Jupyter notebook session by re-executing all cells to restore kernel state for continued computation in HPC environments.

Instructions

Resume a notebook: re-execute all cells to restore kernel state.

Args: experiment_name: Name for this session. notebook_path: Path to existing notebook to resume.

Returns: Dict with session_id, notebook_path, job_id, hostname, errors.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
experiment_nameYes
notebook_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool performs re-execution of all cells to restore kernel state, which implies mutation and computational effects. However, it doesn't mention permissions, rate limits, or error handling details, leaving gaps for a tool with significant behavioral impact.

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 front-loaded with the core purpose in the first sentence, followed by structured sections for Args and Returns. Every sentence adds value without redundancy, making it efficient and well-organized for quick comprehension.

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 complexity (resuming sessions with re-execution), no annotations, and an output schema that documents return values, the description is mostly complete. It covers purpose, parameters, and returns, but could benefit from more behavioral context like prerequisites or side effects to fully guide usage.

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

Parameters4/5

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

The description adds meaningful semantics beyond the input schema, which has 0% coverage. It explains that 'experiment_name' is for naming the session and 'notebook_path' is the path to an existing notebook to resume, clarifying their roles. Since there are only 2 parameters and the description covers both adequately, it compensates well for the schema's lack of descriptions.

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 specific action ('resume a notebook: re-execute all cells to restore kernel state'), identifies the resource (notebook), and distinguishes it from sibling tools like 'start_new_session' and 'start_session_continue_notebook' by emphasizing re-execution for restoration.

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 usage context by specifying 'resume a notebook' and 'existing notebook to resume', suggesting it's for restarting prior sessions rather than creating new ones. However, it lacks explicit guidance on when to use alternatives like 'start_new_session' or 'start_session_continue_notebook'.

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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