ipynb_split_notebook

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

With no annotations, the description carries the full burden. It discloses the splitting criteria (markdown_headers vs cell_count), the output directory, and the return format (success/files_created/error). However, it fails to mention whether the original file is modified, if existing files are overwritten, how output files are named, or any permission requirements. This is partial transparency but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized: a one-sentence purpose, followed by a compact Args list and a Returns note. It is front-loaded with the main action and every sentence serves a purpose. No fluff or redundancy.

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

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

For a tool with 3 parameters and moderate complexity, the description covers all parameters and the return value. It lacks some operational details such as output file naming, overwrite behavior, and specific error conditions beyond returning an 'error' key. However, the core functionality is well specified, making it mostly complete for the intended use.

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

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

Schema description coverage is 0%, so the description must fully explain parameters. It does so clearly: ipynb_filepath (path, absolute preferred), output_dir (directory for output), and split_by (allowed values 'markdown_headers' or 'cell_count'). This adds meaning beyond the bare schema and fully compensates for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Split a Jupyter Notebook (.ipynb) into multiple files by criteria.' It uses a specific verb (split) and resource (Jupyter Notebook), and the criteria are further detailed via the split_by parameter. This distinguishes it from sibling tools like merge_notebooks or extract_cells, which perform different operations.

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

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

The description implies when to use the tool (when you need to split a notebook by markdown headers or cell count) but does not explicitly discuss alternatives or exclusion cases. It provides no guidance on when not to use it or how it compares to related tools like extract_cells or apply_to_notebooks, so it only meets the 'implied usage' level.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.