jupyter_restart_kernel

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

With no annotations provided, the description carries full burden. It discloses that restarting clears all state, which is crucial. However, it omits details like whether the kernel must be running, if the kernel ID becomes invalid after restart, or any side effects such as loss of unsaved data.

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 a single sentence, concise and front-loaded, with every word contributing value (verb, resource, and key behavioral effect). No wasted text.

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 the tool's simplicity (1 parameter, output schema exists), the description is fairly complete. It explains purpose and the main behavioral aspect. With output schema present, description does not need to detail return values. A minor lack of prerequisites or post-conditions prevents a 5.

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 schema covers 100% of the single parameter (kernel_id), and the description adds no additional meaning beyond the schema. Baseline score of 3 is appropriate.

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 action 'Restart a kernel' and the key effect 'clears all state', which distinguishes it from similar tools like jupyter_interrupt_kernel (which does not clear state) and jupyter_start_kernel (which starts a new kernel).

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 when a fresh kernel state is needed, but does not explicitly specify when to use this tool versus alternatives like jupyter_interrupt_kernel or jupyter_start_kernel. No exclusions or prerequisites are mentioned.

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