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ipynb_reorder_cells

Reorder cells in a Jupyter Notebook (.ipynb) by providing a new index mapping that specifies the desired order.

Instructions

Reorder cells in a Jupyter Notebook (.ipynb) by providing new index mapping.

Args: ipynb_filepath: Path to Jupyter Notebook (.ipynb) file (absolute path preferred) new_order: List of indices in desired order (0-based indexing)

Returns: Dict with 'success' or 'error' key

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
new_orderYes
ipynb_filepathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It only mentions the return format ('Dict with 'success' or 'error' key') and does not disclose whether the file is modified in place, the validation rules for 'new_order', or potential side effects. This is insufficient for a mutation tool.

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 and well-structured with Args and Returns sections. It front-loads the core purpose in the first sentence and contains no filler. Every sentence contributes useful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has moderate complexity: it accepts two parameters and returns a simple dict. The description covers inputs and return shape, which is adequate for a straightforward operation. However, it lacks critical context about validation, file mutation behavior, and error handling, especially since there are no annotations to fill the gap.

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 input schema provides only names and types, so the description adds valuable meaning: it explains that 'ipynb_filepath' is a path to a .ipynb file (absolute path preferred) and that 'new_order' is a list of indices using 0-based indexing. This goes beyond the schema, though it omits constraints like requiring a full permutation of cell indices.

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 ('Reorder cells') and the resource ('Jupyter Notebook (.ipynb)'), and specifies the method ('by providing new index mapping'). This makes it easy to understand what the tool does and distinguishes it from sibling cell manipulation tools like insert, delete, or replace.

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?

The description implies the tool is used when reordering notebook cells, but it does not explicitly state when to use it versus alternatives, nor does it mention exclusions or conditions. No reference to sibling tools or scenarios where another operation would be more appropriate is provided.

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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