jupyter_execute

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It mentions execution via websocket channels but fails to describe side effects, error handling, timeout behavior, or what happens upon successful execution. This leaves significant gaps for an agent.

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 concise at two sentences, but it lacks structure. Important details like parameter semantics or usage examples could be added without sacrificing conciseness. It is front-loaded with the action, which is good.

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 has 3 parameters (2 required), no output schema, and siblings that likely share similar functionality, the description is incomplete. It does not explain return values, error states, or how this tool differs from the many related tools in the sibling list.

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 input schema has zero description coverage for its parameters. The description does not add any meaning to kernel_id, code, or timeout_s beyond their names and types. For example, it does not explain what code format is expected or how timeout_s is applied.

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 'Execute' and the resource 'code on a Jupyter kernel', making the basic purpose obvious. However, it does not explicitly differentiate this tool from siblings like jupyter_execution_submit, which may have overlapping functionality.

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 lists required environment variables (JUPYTER_BASE_URL, JUPYTER_TOKEN) but provides no guidance on when to use this tool versus alternatives such as jupyter_execution_submit or jupyter_execution_status. No conditions or exclusions are mentioned.

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