ipynb_update_metadata

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

Since there are no annotations, the description carries the full burden. It discloses that the 'metadata' dictionary is 'merge'd, implying it doesn't replace the entire metadata but merges fields. It also states the return format (Dict with 'success' or 'error' key). However, it omits side-effect details such as in-place file modification, permissions required, or whether the change is reversible. It provides some useful behavior but not a comprehensive disclosure.

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 tightly structured as a docstring with a one-line purpose followed by Args and Returns sections. Each sentence provides essential information without fluff. The format is easy to parse, and all three parameters are explained in a consistent, compact way.

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 of moderate complexity (3 parameters, output schema indicated), the description covers the key aspects: what it does, parameter meanings, and return shape. The presence of many sibling tools means the description could more explicitly differentiate from ipynb_sync_metadata or ipynb_replace_cell, but the core usage is well-specified. It lacks edge-case notes (e.g., error conditions) but is generally complete.

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 each parameter. It does: 'ipynb_filepath' is described as the path to the .ipynb file (absolute preferred), 'metadata' is the dictionary to merge, and 'cell_index' is the index of the cell (None for notebook metadata). This goes far beyond the raw schema types and gives meaningful semantics for correct usage.

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 action: 'Update Jupyter Notebook (.ipynb) or cell metadata.' This is a specific verb-resource combination that distinguishes it from sibling tools like ipynb_get_metadata (read) and ipynb_sync_metadata (sync). The mention of cell_index clarifies the two possible target scopes (notebook vs. cell), making the purpose unambiguous.

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 provides clear context on when to use the tool: updating metadata (either notebook-level or cell-level via cell_index). It explains that using cell_index=None targets notebook metadata, while a numeric cell_index targets a specific cell. However, it does not explicitly mention alternatives or when not to use it, but the implied usage from the description is sufficient for selecting it over read-only or other mutation tools.

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