get_dataset

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

No annotations are provided, so the description carries the burden. It discloses that the tool can both read and update (modify) a dataset, which is key behavioral info. Does not cover rate limits, authorization, or idempotency, but it adds context beyond the schema.

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, using bullet points for clarity. Every sentence is purposeful: first line states dual purpose, then bullets for read vs update, then note on creation. 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 4 parameters and existence of output schema, the description covers the main usage scenarios. It mentions the creation alternative. Minor gap: does not mention that updating requires an existing dataset or validation, but overall sufficient.

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 must compensate. It explains that project_id and dataset_id are for identification, and name and description are used only for update/rename. This adds essential meaning beyond the schema's property definitions.

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 can both fetch and update a dataset, with specific verbs and resources. It distinguishes from sibling 'complete_upload' by noting that datasets are created there.

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 provides clear context on when to use the tool: for reads with only project_id and dataset_id, and for updates with optional name/description. It explicitly says datasets are created via complete_upload, so not for creation. Lacks mention of listing alternatives but adequate.

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