ipynb_extract_cells

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

With no annotations provided, the description must carry the full burden. It lists parameters and return keys, but omits potential side effects (e.g., whether input notebooks are modified, whether output file is overwritten) and details about how 'pattern' matches against cell content. This leaves notable gaps in behavioral understanding.

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 well-structured with a one-line summary, an Args section, and a Returns section. Every sentence is useful and there is no fluff. This format is ideal for machine parsing and agent 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 no annotations and a large sibling set, yet the description does not explicitly differentiate from similar operations like ipynb_filter_cells or ipynb_merge_notebooks. It also lacks critical context such as overwrite behavior and exact matching semantics. Despite having a Returns section, the overall context is incomplete for a tool of this complexity.

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 input schema has no parameter descriptions (0% coverage), but the description provides meaningful explanations for all four parameters, noting optionality and path preferences. It does not specify valid cell_type values or what pattern matches against, so it doesn't fully compensate for the schema's silence, but it adds substantial value.

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 a specific action ('Extract matching cells from multiple Jupyter Notebooks') and the output resource ('into a new notebook'). This distinguishes it from siblings like ipynb_merge_notebooks or ipynb_filter_cells, which operate on entire notebooks or a single notebook respectively.

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 for extracting filtered cells from multiple notebooks into one, but does not explicitly state when to use this tool over sibling tools or provide exclusion criteria. It leaves the agent to infer the context from the action alone.

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