metadata_ai__apply_localizations

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

With no annotations provided, the description carries the full burden and delivers the two most operationally critical behaviors: pre-flight validation against Apple's character limits making a bad file change nothing at all, and the non-atomic per-language submission where a mid-way rejection still leaves earlier writes in place with errors naming exactly which ones landed. An agent needs both facts to set user expectations and interpret failures.

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?

Four sentences with no wasted words, front-loaded with the core purpose and then covering formats, validation safety, and failure semantics in order of importance. Every sentence carries a distinct, decision-relevant piece of information.

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?

There is no output schema, so the description is the sole carrier of result semantics. It covers input formats, the all-or-nothing validation guarantee, and the partial-failure error behavior. It stops just short of describing the success-return shape, but the essential operational behaviors an agent needs are present.

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 100%, so the baseline is 3. The description reiterates the CSV/JSON file formats in prose, but the schema's file_path description already documents the CSV columns and JSON structure, so the description adds no meaningful parameter-level semantics beyond what the schema provides.

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 opens with a specific verb+resource: 'Write store text for several languages at once from a file the user prepared.' This clearly distinguishes it from the sibling metadata_ai tools (audit_localizations, draft_translation) by emphasizing that values come from the user's prepared file rather than being generated or checked.

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 establishes the use case well: apply pre-existing user-provided file values in bulk, with the contrast 'rather than retyped' signaling when this tool fits. However, it never explicitly names alternatives like metadata_ai__draft_translation for generating text or app_store_version_localizations__update for a single manual edit, nor states when NOT to use this tool.

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