kernel_discover

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

With no annotations provided, the description carries the burden. It discloses the return format (list of dicts with name, display_name, language, source) and the conditional behavior when scan_dir is provided. However, it doesn't disclose what happens if no kernels are found, whether errors can occur, or how the scan operates (speed, blocking behavior). The core behaviors are disclosed but with limited depth.

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 focused and informative—roughly 5 sentences covering purpose, output format, usage linkage, and the optional parameter. It's front-loaded with the core purpose and output, then details scan_dir behavior. No wasted words, though it could be marginally tightened.

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 read-only discovery tool with one optional param and an output schema present, the description is fairly complete. It explains the return structure, links to the sibling kernel_start workflow, and clarifies scan_dir behavior. The presence of an output schema reduces the burden of explaining return values. Minor gaps remain around error/edge cases, but overall adequate for this tool's complexity.

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 has 0% description coverage, so the description must compensate. It does: scan_dir is explained as a path that triggers checking for a .venv with ipykernel, and the resulting 'venv:<path>' naming convention is disclosed. With one optional parameter, the description adequately covers its semantics beyond the bare schema definition.

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 verb (Discover) and resource (Jupyter kernel specs and project venvs). It distinguishes from siblings (kernel_start, kernel_status, etc.) by listing the return values that guide subsequent kernel_start usage. It's specific about what's returned (list of dicts with keys) and how to use the output.

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 clear practical guidance: tells the agent to use the returned 'name' value as kernel_name for kernel_start, and explains when scan_dir matters (checks for .venv with ipykernel). This gives good context for when this discovery tool should be invoked, though it doesn't explicitly state when NOT to use it or name alternative tools.

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