ipynb_list_cells

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

With no annotations, the description must carry the full burden. It discloses the return format (dict with 'cells' list or 'error' key) and that it provides content previews. However, it does not explicitly state that the operation is non-mutating, what the preview length is, or any potential performance implications for large notebooks. This leaves some gaps in behavioral transparency.

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, with the purpose stated upfront and then Args/Returns sections. It avoids unnecessary repetition and every sentence earns its place. The format is easy to scan and understand.

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 simple tool with one parameter and no nested objects, the description covers the essential information: what it does, what the parameter means, and what it returns. It could improve by adding details like whether cells are returned in order or what constitutes a 'content preview,' but overall it is complete enough for effective 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?

The parameter description adds value beyond the schema, which only specifies 'type': 'string'. The description explains that ipynb_filepath is the path to the Jupyter Notebook file and adds a useful preference: 'absolute path preferred.' This provides context not available in the schema, though it could include more detail like accepted formats or examples.

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 function: 'List all cells in a Jupyter Notebook (.ipynb) with indices, types, and content previews.' This uses a specific verb (list), identifies the resource (cells in a notebook), and specifies the output details, distinguishing it from sibling tools like ipynb_get_cell which targets a single cell.

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 usage by indicating it lists all cells, but it does not explicitly state when to use this tool over alternatives or mention any exclusions. For example, there is no guidance like 'use ipynb_get_cell for a single cell' or 'use ipynb_search_cells for filtered access.' The context is clear but basic, so a score of 3 is appropriate.

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