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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds meaningful behavior beyond that: full-file downloads require the user to configure their own DD_TOKEN, and the token must never be included or requested, which is a valuable security constraint an agent would not otherwise know.

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?

Two short sentences with no filler, and the primary purpose is front-loaded ahead of the secondary token warning. Efficient and appropriately sized for a simple tool.

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?

No output schema exists, and the description sufficiently conveys that the return is generated Python example code plus the token-handling caveat. For a single-parameter, read-only generation tool this is close to complete, with minor gaps around how to supply or obtain a valid slug.

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%, but there is only one parameter, so the burden is light. The description clarifies that the slug refers to a 'published dataset,' adding semantic context the bare string schema lacks, but it does not give format or source guidance for obtaining a valid slug.

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?

States a specific verb ('Generate') and concrete deliverable ('a small Python example') tied to the dataset slug, so the agent knows exactly what it produces. However, it does not distinguish itself from the sibling get_notebook_example, which is a closely related code-generation tool, leaving the selection boundary ambiguous.

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 explicit statement of when to use this tool versus get_notebook_example or preview_dataset, and no when-not guidance. The phrase 'using the published dataset slug' implies a prerequisite but does not constitute routing guidance.

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