ai_apply_schema

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only restates the apply action and notes the default branch_name, which is already in the schema default. It does not disclose whether the operation is destructive, whether it commits changes, or how it handles invalid SQL.

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 compact and well-structured: a one-sentence purpose followed by a clean argument list. It contains no redundant or fluff content and is easy to scan.

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's potentially schema-modifying nature, the description is too thin. It lacks context about the workflow with ai_preview_schema, what happens after applying (e.g., commit, migration), or any prerequisites. The presence of an output schema reduces the need for return-value explanation, but behavioral and workflow context is still missing.

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's Args section is the only source of parameter explanations. It describes all five parameters, clearly defining the allowed mode values ('create' or 'modify') and the branch_name default, thus compensating for the 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 tool applies AI-generated SQL to a schema in a specified branch, using the verb 'применить' (apply) and naming the target resource. It implies a distinct action from sibling preview tools by using 'apply' vs 'preview', but does not explicitly name alternatives.

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 implies usage when you have AI-generated SQL and want to apply it to a schema branch, but provides no explicit guidance on when to use it instead of ai_preview_schema or when not to use it. There are no exclusions or alternative tool references.

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