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ipynb_get_notebook_info

Get key notebook metadata from a .ipynb file: cell count, cell types, kernel, format version, and file size. Use it to assess notebook contents quickly.

Instructions

Get summary information about a Jupyter Notebook (.ipynb).

Args: ipynb_filepath: Path to Jupyter Notebook (.ipynb) file (absolute path preferred)

Returns: Dict with cell_count, cell_types, kernel, format_version, file_size or 'error' key

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ipynb_filepathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are provided, so the description carries the burden of disclosing behavior. It specifies the return dictionary structure, including an 'error' key, which signals failure handling. It also notes 'absolute path preferred', providing practical context. However, it does not explicitly state read-only behavior or discuss side effects, though the verb 'Get' strongly implies non-mutation.

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 purpose in the first sentence and contains no unnecessary words or repetition. Every element earns its place.

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?

For a simple read-only info tool with one parameter and an output schema, the description is largely complete. It covers the purpose, parameter semantics, and return value shape. Minor gaps include not specifying units for file_size or detailing error conditions beyond the 'error' key, but these are not critical for a tool of this simplicity.

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

Parameters5/5

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

Schema coverage is 0% for the single parameter, and the description fully compensates by explaining what `ipynb_filepath` is and adding a usage hint ('absolute path preferred'). This goes beyond the bare schema type and provides meaningful guidance for correct invocation.

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 verb+resource: 'Get summary information about a Jupyter Notebook (.ipynb)'. It is specific and distinct from siblings like ipynb_read_notebook (full content) and ipynb_get_metadata (metadata only), as it lists specific summary fields (cell_count, cell_types, kernel, format_version, file_size).

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 implicitly indicates usage for obtaining a high-level overview, but it does not provide explicit guidance on when to use this tool versus alternatives like ipynb_read_notebook or ipynb_get_metadata. No exclusions or alternative mentions are present, so usage is implied rather than explicit.

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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