ipynb_list_available_kernels

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

With no annotations, the description carries the full burden. It only states the return type ('Dict with 'kernels' list') and the general scope ('common' configurations). It does not confirm that the operation is read-only, side-effect-free, or how 'common' is determined, leaving important behavioral traits undisclosed.

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 extremely concise, with a single purpose sentence and a return-type sentence. No wasted words; the structure is clean and front-loaded.

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 zero-parameter listing tool, the description provides the essential information: what it lists and the return shape. The output schema exists and covers return structure. A slight gap is the ambiguity of 'common' (e.g., does it include all discovered kernels or a filtered subset?), but overall it is sufficiently 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?

The tool has zero parameters, and the schema is empty, so no parameter descriptions are needed. The description adds no parameter info, but that is acceptable given the baseline of 4 for parameterless tools with full schema coverage.

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 lists Jupyter notebook kernel configurations, using a specific verb ('list') and resource ('kernel configurations'). It is distinguishable from sibling tools that operate on cells or metadata, though it doesn't explicitly address what 'common' means or contrast with a hypothetical 'list all' variant.

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?

No guidance is provided on when to use this tool versus alternatives. While sibling tools like ipynb_set_kernel imply a use case (check available kernels before setting one), the description itself offers no explicit when-to-use or when-not-to-use instructions.

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