ipynb_search_cells

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

With no annotations, the description carries the transparency burden. It discloses the return structure (Dict with 'results' list or 'error' key) and clarifies that the pattern supports regex, which are useful behavioral details. It does not explicitly confirm that the operation is read-only, but the verb 'Search' strongly implies a non-destructive read operation, which is sufficient for a tool of this nature.

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 compact, with clear Args and Returns sections. Each sentence adds necessary information without redundancy, and the structure makes it easy for an agent to parse parameter names and expected output.

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 search tool, the description covers purpose, parameters, and return type. An output schema exists, so return-value details need not be explained. The primary gap is the lack of explicit usage differentiation from sibling search tools, but overall it provides sufficient information for an agent to select and invoke the tool.

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 description provides semantics for all three parameters: ipynb_filepath as a path (absolute preferred), pattern as a regex-supporting search string, and case_sensitive as a flag controlling case sensitivity. Since the schema has no property descriptions (0% coverage), this description compensates well by adding meaning to each parameter.

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 uses the specific verb 'Search' and identifies the resource as 'cell content of a Jupyter Notebook (.ipynb)', clearly indicating a targeted search operation. It distinguishes from sibling tools like ipynb_search_replace_all by not mentioning any replacement functionality, but does not explicitly differentiate from ipynb_search_notebooks, which could also search notebook content.

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 context by stating it searches cell content of a single notebook, but does not explicitly state when to use this tool over alternatives like ipynb_search_notebooks (for multiple notebooks) or ipynb_search_replace_all (for search-and-replace). There are no excluded cases or alternative tool references.

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