notebook_list_kernels

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

No annotations provided, so description carries full burden. It discloses return format (dict of name to display name) and potential module issues with solution. However, it doesn't mention any permissions or failure modes beyond that.

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?

Description is concise with only essential sentences and a code example. Every sentence adds value, well-structured with clear sections.

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?

Given 0 parameters and output schema present, the description fully explains the return structure, usage, and provides troubleshooting, making it complete for an agent.

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 0 parameters, so baseline is 4 per instructions. No parameter info needed, but the description adds no semantics beyond that baseline.

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 'List every Jupyter kernel spec installed on this machine' with a specific verb and resource. It distinguishes from siblings like notebook_list_active_kernels which lists active kernels rather than all specs.

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?

It provides explicit guidance on using the kernel_name from the output as an argument to execution tools, and includes troubleshooting advice for ModuleNotFoundError. It could mention not using it when you need active kernel info, but context from sibling names mitigates this.

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