jupyter_inspect

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

With no annotations, the description must fully disclose behavior. It mentions returning 'repr + type' but does not state whether the call is read-only, non-blocking, or idempotent. No side effects or safety cues are provided.

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 two sentences plus a bullet list. The first sentence carries the core purpose and is front-loaded. The requirements list is relevant but adds little value to tool selection. Overall, it is efficient with no wasted words.

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 absence of an output schema and parameter descriptions, the description falls short. It does not explain what the tool returns (repr, type, or other data), error handling, or any constraints. The user must rely on external knowledge to use it correctly.

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 description adds no meaning beyond the input schema. Schema coverage is 0%, and the description does not explain kernel_id, expression, or timeout_s. The parameter names and required status are insufficient for correct usage without further context.

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 tool's purpose: 'Inspect an expression on a kernel using user_expressions (repr + type).' This is specific about the verb (inspect), resource (kernel), and method (user_expressions). It distinguishes from sibling tools like jupyter_execute or jupyter_get_kernel, which have different actions.

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 environment variable requirements (JUPYTER_BASE_URL, JUPYTER_TOKEN) but provides no guidance on when to use this tool versus alternatives like jupyter_execute or notebook_analyze. There is no explicit statement about use cases or exclusions.

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