jupyter_list_kernels

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

Without annotations, the description carries the full burden. It reveals the return format and confirms it's a read-only list operation. However, it lacks details about potential side effects or authentication requirements, though these are minimal for a list tool.

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 two short sentences that cover the action and return value. No unnecessary words or verbosity.

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 list tool with no parameters and an output schema, the description provides sufficient context: it states what it does and what it returns. No additional information is needed.

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 description correctly includes none. Per the baseline rule for 0 params, a score of 4 is appropriate as there is no need for additional parameter documentation.

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 it lists all running Jupyter kernels, specifying the resource (kernels) and action (list). It distinguishes from sibling tools like jupyter_start_kernel or jupyter_stop_kernel, which perform different operations.

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 checking running kernels but does not provide explicit guidance on when to use vs alternatives, such as jupyter_get_notebook_info or jupyter_start_kernel. No when-not-to-use context is given.

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