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VincentKaufmann

noapi-google-search-mcp

google_translate

Translate text between languages. Specify target language and optionally source language for accurate translations.

Instructions

Translate text from one language to another using Google Translate.

Sample prompts that trigger this tool: - "Translate 'hello world' to Japanese" - "How do you say 'thank you' in French?" - "Translate this to Spanish: The weather is nice today" - "What does 'Guten Morgen' mean in English?" - "Translate 'I love programming' to Korean"

Args: text: The text to translate. to_language: Target language (e.g. "Spanish", "Japanese", "French", "German", "Korean", "Chinese", "Arabic"). from_language: Source language (optional, auto-detected if empty).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
to_languageYes
from_languageNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions that 'from_language is optional, auto-detected if empty', but lacks details on rate limits, authentication, or side effects. Minimal behavioral disclosure.

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

Conciseness4/5

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

The description is well-structured with sample prompts and an Args list. It is slightly verbose due to examples, but every sentence adds value. No wasted words.

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

Completeness4/5

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

For a simple translation tool with an output schema, the description covers the essential behavior: what it does, required parameters, and sample usage. It could mention the return format, but the output schema presumably handles that.

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

Parameters4/5

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

Schema description coverage is 0%, but the description explains each parameter's role (text, to_language, from_language) and notes the optionality of from_language. This adds meaning beyond the schema's type and title fields.

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

Purpose5/5

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

The description clearly states 'Translate text from one language to another using Google Translate', with a specific verb and resource. It distinguishes from all sibling tools, as none are translation-related.

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

Usage Guidelines4/5

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

Sample prompts effectively illustrate when to use the tool, such as translating phrases or asking for meanings. No explicit alternatives or exclusions are given, but the context is clear.

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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