ipynb_read_notebook

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

No annotations exist, so the description carries full responsibility. It discloses the return format (dict with cell_count, cell_types, kernel_info, format_version), mentions the error key on failure, and notes the preference for absolute paths. This is solid transparency for a read-only tool.

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 Args and Returns sections. Every sentence provides value, no fluff, and the key information is front-loaded in the first sentence.

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 single-parameter read tool with an output schema, the description covers the essential aspects: purpose, parameter meaning, return structure, and failure behavior. It is complete and self-contained.

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 compensates by explaining 'ipynb_filepath' as the path to a Jupyter Notebook file and recommends an absolute path. This adds meaningful context beyond the schema's bare string type.

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 it reads a Jupyter Notebook and returns a structure summary. The verb 'Read' and resource 'Jupyter Notebook (.ipynb)' are specific, and the summary output distinguishes it from sibling tools focused on editing or cell-level 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 use when a structure summary is needed, but it does not explicitly mention alternatives or when not to use it. With many sibling tools like ipynb_get_notebook_info or ipynb_list_cells, it would benefit from explicit differentiation, but the context is clear enough for basic selection.

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