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Translate text (offline engine)

translate_text
Read-onlyIdempotent

Translate text between languages using a self-hosted LibreTranslate engine (fully offline, no big-tech API). For whole PDFs with layout preserved, use translate_pdf instead. Example — GET https://ainetcafe.com/t/translate_text?text=hello+world&to=zh

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText to translate (≤5000 chars).
sourceNoSource language code; default "auto".
targetYesTarget language code, e.g. "zh", "en", "ja".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds meaningful behavioral context by stating 'fully offline, no big-tech API', which conveys data-handling traits. The example shows a GET request, but its parameter name is inconsistent with the schema (to vs target), slightly undermining trust, though this is more of a parameter issue.

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 concise with two sentences and an example, but the example is inaccurate (wrong parameter name), so not every sentence earns its place. It is still appropriately front-loaded with purpose and usage guidance, but the factual error prevents a perfect score.

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

Completeness3/5

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 are not needed in the description. The description includes the purpose, an alternative, and an example, but the example's error creates a gap in practical usability. It also lacks information about rate limits or error handling, but these are not required for simple translation. The tool is relatively simple, so overall completeness is adequate but flawed by the example.

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

Parameters1/5

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

Although the input schema covers 100% of parameters with descriptions, the example in the description uses 'to' instead of the schema's 'target' parameter name. This is a direct contradiction with the schema and would mislead an agent into using an invalid parameter. The description actively undermines parameter understanding, so despite high schema coverage, the score is a 1.

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 it translates text between languages using a self-hosted LibreTranslate engine, and explicitly mentions 'fully offline, no big-tech API' to distinguish it from other translation tools. It also points to translate_pdf for whole-PDF translation, which differentiates it from a sibling tool. This is a specific verb+resource+scope with clear differentiation.

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?

The description gives a clear usage context: translate text online via a self-hosted engine, and explicitly names an alternative (translate_pdf) for PDFs with layout preserved. It doesn't discuss when not to use this tool for other formats like SRT or JSON, but the provided alternative and the offline context give sufficient guidance.

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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TDQS

A4.3/5.0
Disambiguation5/5

Each tool targets a distinct resource or action: plain text, PDF, SRT, JSON, grammar checking, and job polling. There is no meaningful overlap, and the descriptions explicitly call out when to use one over another (e.g., translate_text vs translate_pdf).

Naming Consistency5/5

All tools follow a clear verb_noun pattern in lowercase snake_case: check_grammar, check_job, translate_i18n_json, translate_pdf, translate_srt, translate_text. The two verbs (check_ and translate_) correspond to their functional groups, making the naming predictable and easy to navigate.

Tool Count5/5

Six tools is a well-scoped count for a translation-focused server. Each tool has a distinct purpose and earns its place; the set is neither bloated nor too thin.

Completeness4/5

The surface covers the core translation formats (text, PDF, subtitles, i18n JSON) plus async job status and grammar checking. Minor gaps exist, such as support for other document formats (e.g., DOCX) or a language-list endpoint, but these are not essential to the apparent purpose.