ipynb_merge_notebooks

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

The description discloses the return value (dict with success/total_cells/notebooks_merged/error) and the add_separators option, but lacks important behavioral details such as whether the output file is overwritten, whether input notebooks are modified, or how cells/metadata are handled. With no annotations provided, the description carries the full burden for safety disclosures, and this gap prevents a higher score.

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 concise and well-structured: a one-sentence purpose followed by an Args/Returns breakdown. Every sentence is informative, and there is no fluff or repetition. The front-loaded purpose aids quick comprehension.

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?

The tool has 3 parameters, all of which are described, and the return value is specified. For a merge operation, this is nearly complete. However, edge cases like handling an existing output file or empty input lists are not addressed, leaving a small gap in completeness.

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 coverage is 0%, but the description fully compensates by explaining each parameter: output_ipynb_filepath (path for merged notebook), input_ipynb_filepaths (list of notebook paths), and add_separators (whether to add separator cells). This adds meaning far beyond the raw schema, making parameter semantics excellent.

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: 'Merge multiple Jupyter Notebooks (.ipynb) into one.' This specific verb+resource combination immediately distinguishes it from siblings like ipynb_split_notebook (which splits) and ipynb_apply_to_notebooks (which applies operations). No ambiguity.

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 makes the use case obvious: when you need to combine multiple notebooks into a single one. It does not explicitly mention alternatives or when not to use it, but the context is clear. Without explicit exclusions or mentions of sibling tools, it stops short of a 5.

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