jupyter_connect_notebook

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

With no annotations, the description carries full burden. It explains the main behavior (connect or create session) and the return value. However, it does not disclose potential side effects, error conditions, or authentication requirements, leaving gaps in transparency.

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 very concise, consisting of two efficient sentences. Every sentence adds value: the first states the purpose, the second explains the return value usage. No wasted words or redundancy.

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 a single parameter and an output schema (not shown), the description is largely complete. It covers the core purpose, usage flow, and output usage. Minor omissions include error scenarios and path specifics, but overall it is sufficient for a simple tool.

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 already describes notebook_path (100% coverage). The description does not add any additional meaning beyond what is in the schema, such as path format or validation rules. Baseline score of 3 is appropriate.

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 connects to a notebook's kernel, creating a session if needed. It uses specific verbs and explains the return value for subsequent execute_code calls. However, it does not explicitly differentiate from sibling tools like jupyter_start_kernel, which serves a different but related purpose.

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 indicates when to use the tool (to get a kernel session for a notebook) and what to do with the result (use kernel_id for execute_code). It does not mention when not to use it or provide alternatives, such as using jupyter_start_kernel for standalone kernel operations.

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