translate

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

With no annotations provided, the description carries the full burden and does well by disclosing behavioral traits: it explains the engine uses team resources (translation memory, style guides, brand voice), returns additional info (TM match, rationale), and supports back-translation for verification. It lacks details on rate limits or error handling.

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 appropriately sized and front-loaded with the core purpose, followed by a structured parameter list. Every sentence adds value, though the parameter section is lengthy but necessary given the complexity. Minor room for tightening introductory text.

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 complexity (10 parameters, no annotations) and the presence of an output schema, the description is largely complete: it covers purpose, behavioral context, and parameter semantics. It could improve by mentioning output structure or error cases, but the output schema likely handles return values.

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 fully. It provides detailed semantics for all 10 parameters, including examples (e.g., 'French', 'fr'), defaults (e.g., source language defaults to English), and usage guidance (e.g., context for audience targeting). This adds significant value beyond the bare schema.

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's purpose as translating text using Nativ's AI localization engine, specifying it leverages translation memory, style guides, and brand voice. It distinguishes from siblings like 'translate_batch' (batch processing) and 'search_translation_memory' (searching rather than translating).

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 for AI-powered translation with contextual enhancements but does not explicitly state when to use this tool versus alternatives like 'translate_batch' for batch operations or 'search_translation_memory' for lookup. No exclusions or prerequisites are mentioned.

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