ipynb_set_kernel

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

With no annotations, the description must disclose behavioral traits. It only mentions the return format ('Dict with 'success' or 'error' key') but omits side effects like in-place file modification, overwriting of existing kernel metadata, or validation of the kernel name. This under-discloses for a mutating operation.

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 structured as a concise docstring with purpose, arguments, and return value. It front-loads the core action and contains no filler, though it has minor whitespace. Overall efficient and easy to scan.

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?

The tool is simple, but the description omits usage context and side effects. For a mutating tool, an agent may not realize the notebook file is modified or need to check available kernels beforehand. Referencing sibling tools like ipynb_list_available_kernels would improve completeness.

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?

Schema description coverage is 0%, so the description compensates by listing all four parameters with brief explanations, including examples for kernel_name and display_name and a default for language. However, it lacks deeper semantics such as allowed values, constraints, or parameter relationships.

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: 'Set kernel specification for a Jupyter Notebook (.ipynb).' This uses a specific verb and resource, distinguishing it from sibling tools like ipynb_list_available_kernels or ipynb_update_metadata.

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?

There is no guidance on when to use this tool versus alternatives. It does not mention prerequisites such as kernel availability, nor does it contrast with related tools like ipynb_list_available_kernels. The usage is implied by the action but not explicitly stated.

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