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Update the user's own profile fields when they ask: timezone, name, headline, company website, LinkedIn URL or X handle. Only pass the fields they asked to change. timezone must be an IANA zone name — translate their words yourself ('ET', 'eastern time', 'Boston time' all mean America/New_York; 'PST' means America/Los_Angeles). Confirm what was set in your reply.

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

Translate an .srt subtitle file into another language while keeping every timestamp and cue number byte-identical. Cues are anchored by index and refilled in code, so the timeline cannot drift — the usual failure mode when you hand a whole .srt to a model.

Translate an i18n JSON locale file, keeping the key structure identical and placeholders ({name}, {{count}}, %s, HTML tags) intact. Pass existing_json to translate only the keys that are missing from it — the incremental sync people usually hand-roll a script for.

Get the status or result of a job started by deep_research, translate_pdf, or make_slides. Poll every 15-30 seconds until status is "done" or "error". While work is pending, follow retry_after_seconds and next_action; when complete, prefer structured_result when present. Example — GET https://ainetcafe.com/t/check_job?job_id=<id-from-a-job-tool>

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":"<pdf-url>","target":"zh"} → poll check_job

Translate text between 13+ languages with an LLM. Arabic-first quality, with formality control (formal/informal) and optional context to disambiguate meaning. Handles both short dictionary-style word lookups and full documents. Returns the translation and, when available, alternative phrasings.

One call: OCR an image, then translate every line into target_lang. Arabic-first OCR and manga-aware Japanese with right-to-left-aware layout, followed by LLM translation. Automatic source-language detection. Provide the image as base64. Ideal for reading foreign documents, signs, manga, or receipts end-to-end in a single step.