Datasets Create Connection

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

Annotations already declare a non-read-only, non-destructive, open-world write, but the description adds genuine context beyond them: which body fields are required vs optional, that the details object may contain write-only secrets, that secrets must come only from the user's own input, and that they are masked in any echoed result or error. It does not cover failure modes (invalid credentials, duplicate name) or post-creation behavior such as syncing, but the secret-handling disclosure is substantial.

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?

Three sentences, all load-bearing: purpose first, then the body composition, then the secret-handling constraint. The phrasing 'body requires name and details...' is slightly telegraphic but not wasteful.

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?

For a single opaque-body mutation tool with an output schema (so return values need not be explained) and annotations covering the safety profile, the description supplies what the schema cannot: field names, optionality, and secret handling. It leaves out authorization prerequisites and failure behavior, which is the main remaining gap.

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% and the single body parameter is an opaque additionalProperties object, so the description carries the full burden. It names the required fields (name, details with warehouse type and settings), the optional fields (description, poolSizes, timeoutSecs, useFriendlyNames), and the secret keys inside details, which is a large amount of otherwise-unavailable semantics. It does not enumerate the valid warehouse type values or the per-type settings shape.

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 first sentence is a clean verb+resource statement ('Create a data warehouse connection') that clearly marks this as the creation counterpart to datasets_delete_connection, datasets_get_connection and datasets_list_connections. It stops short of explicitly contrasting with those siblings (e.g. when to use datasets_test_connection first), so it is clear but not fully differentiated.

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 when-to-use guidance, no prerequisites (required permissions/role, whether the connection must be tested or synced afterward), and no mention of alternatives such as datasets_test_connection or datasets_list_connections. Only the implied semantics of 'create' tell the agent when to call it.

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