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Language.Batch Translate

language.batch_translate

Translates multiple texts into a target language in one API call to support multi-market content. Use it when you need batch localization and accept per-text Cloud Translation API costs.

Instructions

Translate many texts into a target language in one call for multi-market reach. Incurs Cloud Translation API cost per text.

Input Schema

NameRequiredDescriptionDefault
textsYes
source_languageNo
target_languageYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the burden, and it does disclose one real behavioral trait: per-text Cloud Translation API cost. It does not disclose batch size limits, rate limits, whether result order matches input order, or how partial failures are handled — meaningful gaps for a cost-incurring batch operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences with zero waste; the capability is front-loaded and the cost warning follows immediately. Nothing could be trimmed without losing content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be explained, but with 0% parameter coverage and no annotations the description leaves the agent without language-code format, batch limits, or auto-detect behavior. Inadequate for a 3-parameter operation with no structured documentation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for three parameters, so the description must compensate and it does not. It never explains the texts array semantics, nor that an empty source_language (the schema default) means auto-detect, nor the expected language code format for target_language.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clear specific verb and resource ('Translate many texts into a target language') with the batch/in-one-call scope stated up front. It implicitly contrasts with the single-text sibling language.translate, but never names it, so sibling differentiation is left to inference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'for multi-market reach' hints at the use case, but there is no explicit when-to-use versus language.translate, no guidance on when batch is preferable, and no exclusions or prerequisites. Usage is only implied.

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

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