list_dataset_versions

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the tool returns specific output fields (version_number, version_type, qa_status, row counts) and that the backend resolves access from dataset_id when project_id is omitted. However, it does not explicitly state that the operation is read-only, nor does it mention pagination, ordering, or error conditions, relying on the verb 'list' to imply non-destructive behavior.

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 consists of two efficient sentences with no redundancy. The first sentence front-loads the core purpose and output fields, while the second provides essential parameter guidance. Every word earns its place, making it highly concise and well-structured.

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 low complexity (2 params, 1 required, no enums) and the existence of an output schema (which presumably details return structure), the description covers the primary purpose and parameter nuance. It does not mention potential limitations like pagination or ordering, but for a straightforward list operation, the level of detail is sufficient and complete enough for an AI agent to use 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?

Schema description coverage is 0%, so the description must compensate. It adds meaning to the project_id parameter by stating it is optional and that the backend resolves access from dataset_id when omitted. The dataset_id parameter is not elaborated. The mention of output fields aids understanding but falls outside input parameter semantics. Overall, partial compensation for missing schema descriptions.

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 'list' and the resource 'all versions of a dataset', and specifies the fields returned (version number, version_type, qa_status, row counts). This distinguishes it from siblings like get_dataset_version (single version) and list_datasets (different resource), making the tool's purpose unambiguous.

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 guidance on the optionality of project_id but does not explicitly state when to use this tool vs alternatives such as get_dataset_version or list_datasets. There is no mention of conditions, prerequisites, or exclusions, leaving the agent to infer usage context from the tool's name and purpose alone.

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