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netcafe-docs

by com.ainetcafe

Translate a PDF keeping its layout

translate_pdf

Translate a PDF from a URL while preserving the original layout — formulas, figures and two-column academic typesetting stay intact, unlike ordinary translators that flatten the document. Returns a job_id; poll check_job for the download links (translated-only and bilingual side-by-side). Typically 20-60 seconds for a few pages. Powered by PDFMathTranslate (36k stars) hosted at AI NetCafé. Example — tools/call translate_pdf {"url":"","target":"zh"} → poll check_job

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesDirect URL to the PDF (e.g. an arXiv PDF link).
pagesNoHow much to translate. first = 1 page, first5 = first 5 pages (default), all = whole document (slow and expensive).
lang_toNoTarget language, e.g. "Simplified Chinese", "Japanese". Default Simplified Chinese.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes
statusYes
poll_interval_secondsNo

TDQS

A4.1/5.0
Behavior5/5

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

The description discloses asynchronous behavior (returns job_id, poll check_job), expected latency (20-60 seconds), output formats (translated-only and bilingual), and underlying technology (PDFMathTranslate). This adds significant context beyond the sparse annotations.

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, front-loaded with purpose, and includes a concrete example. However, the example contains an incorrect parameter name, which slightly undermines the efficiency and reliability of the information.

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?

With a full input schema and an output schema present, the description appropriately explains the async workflow, polling, and output types. It does not need to detail return values. The minor error in the example does not compromise overall completeness but detracts slightly.

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?

While schema coverage is 100% and the schema already documents all parameters, the description's example uses 'target' instead of the actual parameter 'lang_to', which is misleading. This introduces an ambiguity that could cause incorrect tool invocation.

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 the tool's function: 'Translate a PDF from a URL while preserving the original layout' and highlights unique features (formulas, figures, two-column typesetting) that distinguish it from ordinary translators and sibling tools like pdf_to_markdown. The example flow reinforces the purpose.

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 provides clear context on when to use the tool (for layout-preserving PDF translation from a URL) and contrasts it with 'ordinary translators that flatten the document'. However, it does not explicitly specify when not to use it or name alternative tools among the siblings.

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

A3.7/5.0
Disambiguation5/5

Each tool targets a unique operation—conversions, extractions, translations, and utilities like resume checking or redaction—with no meaningful overlap. The few similar tools (e.g., convert_to_pdf vs. xlsx_to_pdf) are clearly distinguished by input type.

Naming Consistency3/5

Naming mixes conventions: verb_noun (extract_tables, redact_text), noun_to_noun (xlsx_to_pdf, pptx_to_pdf), and unusual forms like doc_translate_cn and what_can_you_do. While snake_case is consistent, the verb/noun pattern is not, making the set slightly less predictable.

Tool Count3/5

With 23 tools, the server sits at the heavy end of the acceptable range. Every tool has a distinct purpose, but the spread across PDF handling, research, audio, and accounting utilities feels more like a miscellaneous collection than a focused suite, which could overwhelm agents.

Completeness4/5

The server covers a broad spectrum of document-processing tasks—conversion, extraction, translation, redaction, and validation—with few dead ends. Minor gaps exist (e.g., no PDF merge/split, no OCR for all scanned PDFs, no explicit delete/update for resources), but core workflows are well supported.

Resources