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List the open-source AI applications hosted and ready to run at AI NetCafé (ainetcafe.com). Each one normally requires local setup (Docker/Python + your own model API key); here they run pre-configured. Use this to find a tool for a task like translating a PDF with formulas intact, generating a PowerPoint file, polishing an academic paper, or running an autonomous research report. Do not call this first when the request already clearly matches compare_models, translate_pdf, deep_research, or make_slides; call that task tool directly. Example — GET https://ainetcafe.com/t/list_apps

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 text into another language, preserving formatting. Capped at 30,000 characters

Parse a CVSS v3.x vector string into a per-metric breakdown plus a recomputed base score. Returns the canonicalized vector, version (3.0 or 3.1), base_score, base_severity (NONE/LOW/MEDIUM/HIGH/CRITICAL), and the eight base metrics: attack_vector (NETWORK/ADJACENT_NETWORK/LOCAL/PHYSICAL), attack_complexity (LOW/HIGH), privileges_required (NONE/LOW/HIGH), user_interaction (NONE/REQUIRED), scope (UNCHANGED/CHANGED), and the three impact metrics confidentiality_impact / integrity_impact / availability_impact (NONE/LOW/HIGH each). When temporal/environmental metrics are explicit in the vector, temporal_score and environmental_score are populated separately. Use to translate raw CVSS strings into agent-friendly attributes without re-parsing the vector grammar yourself, and to verify upstream NVD scoring against the recomputed value. v2 vectors (AV:N/AC:L/Au:N/...) are rejected with 400 — read cvss_v2_vector from cve_lookup if you need v2 detail. Free: 30/hr, Pro: 500/hr. Returns {version, vector, base_score, base_severity, metrics: {attack_vector, attack_complexity, privileges_required, user_interaction, scope, confidentiality_impact, integrity_impact, availability_impact}, temporal_score, environmental_score, summary, verdict}.

Use this when you need to author a variable-section sweep along a spine. Insert a `variableSweep(spine, sections, opts?)` declaration into the user's .kcad.ts immediately before the last top-level return. The result is a Shape — chain `.translate(...)`, `.union(...)`, etc. via `add_feature`. `spine_binding` references an existing variable (Curve3D / Sketch / Vec3[]) in the source; each `sections[i].profile_binding` references an existing Sketch. Sections must be strictly increasing in `t` and span [0, 1]; first t=0, last t=1. Orientation is not exposed by this MCP tool until runtime orientation support is wired. Validates every binding exists in the source via regex before inserting (fast structured error vs capture-time stack). Returns the modified code + diagnostics. Side-effect-free.

RETURNS A LIST OF BOOKS (works on a topic) — NOT passages. PICK THIS to discover which works exist on a subject. → For quotable text use search_translations (exact words) or search_concept (by meaning); if the user already named an author/work, call get_book directly (or list_books to find the ID) — the AI summary + chapter outline is usually the right first answer. Searches titles, authors, subjects, and (as a secondary signal) translated text. Query tips: single distinctive words or short phrases work best ("memory palace", "ouroboros"); quoted phrases match exactly. Each result includes total_matches (full count) + returned (this page) + offset for pagination.

READ PIPELINE step 3 — CITE. Get the exact verbatim text of a single page plus its citation apparatus. ALWAYS use before putting text in quotation marks. The response headline is citation_link (the stable sourcelibrary.org/q/… shortlink) — present it to the user alongside the quote. Render as: > [exact translation text, verbatim] > — [Author], p. [N]. [citation_link] PAGE BREAKS: this corpus is paginated from physical leaves, and nearly one prose page-boundary in five has a sentence running across it — sometimes a word split by a hyphen ("…our move-" / "movements…"). A page that opens or breaks off mid-sentence still reads as complete prose and still carries a perfectly valid citation, so check the continuity field on every response BEFORE quoting: if continues_on_next or continues_from_previous is true, call again with context: true and quote the whole sentence. Quoting a fragment as though it were the author's complete thought is a misattribution even when the page number is right. NON-LATIN SCRIPTS: where the page is Greek, Hebrew, Arabic, Sanskrit, Cyrillic and so on, the response also carries romanized — the romanization of the original — so the citation can be shown in three layers: original → romanized → translation → citation_link. It is AI-generated reading apparatus, not a transcription; quote the source from original or translation, never from romanized. Absent on Latin-script pages and on non-Latin pages not yet romanized. ENGLISH ORIGINALS: where the leaf is already English there is no translation and none is needed — the response omits `translation`, sets `text_source: "ocr_original"`, and the verbatim text is `original` (with a `transcription_note`). Quote it as the source's own words, never as a translation, and expect period spelling and long-s (ſ) — it is an uncorrected transcription of the scan. `text_source` is on every response (`translation` otherwise), so branch on it rather than guessing from pages_translated, which is 0 for an English-original book by construction. TRANSLATED EDITIONS: `original` means the text printed on this leaf, which on a translated edition is the TRANSLATOR's language, not the author's. When the response carries `translation_note`, the chain is stated there — attribute the wording to the translator and do not offer the passage as evidence of what the author wrote in their own tongue. Call list_editions to find an original-language witness of the same work. For several pages of one book at once, use get_quotes.

Get players available to acquire in the specified fantasy league, optionally filtered by position. This is fantasy-league availability, not professional-contract status. Pass a requested count exactly from 1 through 100; for more than 100, state the limit and ask the user to narrow the request or accept 100. Prefer the canonical fields: every response carries leagueId, seasonYear, position, count, ordering, capabilities, and ownershipScope; entries carry team (real-life club, null when none) and id (platform player id as a string, when supplied) on every platform, and ESPN entries add acquisitionState ("free_agent", "waivers", or null when the platform cannot determine the subtype) plus waiverClearsAt (ISO time); legacy platform fields remain alongside for compatibility and should not be re-explained. ownershipScope "platform_global" means percentOwned/percentStarted cover all leagues on that platform — never ownership within the selected league. An ESPN-wide started rate is never conditional on the player being rostered. Label every reported percentage as an ESPN-wide roster/start rate or Yahoo-wide market rate. Translate ownership scope silently into that provider-wide wording; never print the ownershipScope key, platform_global enum, or get_free_agents tool name. If capabilities marks rates unavailable, write "[Provider] market ownership rate: not provided"; do not print a missing response field name or null value, call get_players, or offer a lookup. When acquisitionState is null or not present, call rows "available players," never specifically free agents or waivers, and do not promise an immediate add. A returned player is already confirmed available in that league. Use get_roster only when the current request separately asks who owns a player; never offer it after an available-player result. Do not include injuryStatus or any injury detail unless the user asks for it; when asked, verify current web evidence and translate provider codes into plain language. State acquisition status in plain language from acquisitionState ("a free agent", "on waivers"); never print raw codes — neither provider codes such as FREEAGENT or WAIVERS nor canonical values like free_agent verbatim. Use current web evidence before adding analysis or pickup recommendations. Use established session context (call get_user_session only if needed), then get_league_info for the selected league; fan out once per league for comparisons. Requires authentication on ESPN/Yahoo; Sleeper uses the public API. Read-only. Current date is 2026-09-09. Hard stop: after satisfying a returned-list or field-explanation request, end the answer immediately after the requested facts. Remove every closing question or offer to do more work, including roster checks, lineup-fit checks, comparisons, rankings, recommendations, role or health analysis, trends, or outlooks; never append "if you want", "tell me which player", or a similar invitation unless the user's current request explicitly asks for that additional work.

Search for medical conditions, diseases, and health problems in ICD-11 (International Classification of Diseases, 11th Revision). Use this tool to: - Find ICD-11 codes for diagnoses - Search for diseases by name or keyword - Look up conditions in multiple languages Set `language` for WHO's official translations — e.g. `language: "pt"` searches and returns the official Portuguese (pt-BR) ICD-11 labels. Never machine-translated. Returns matching entities with codes, titles, and relevance scores.

Search for MeSH (Medical Subject Headings) descriptors. Use this tool to: - Find MeSH terms for indexing medical literature - Look up subject headings for PubMed searches - Find controlled vocabulary terms Set `language` to request NLM's official translations where they exist (e.g. `language: "pt"` for Portuguese labels); content is never machine-translated. Returns matching descriptors with MeSH IDs and labels.

Translates audio into English.

Translate a document into another language with the original layout and formatting preserved (DOCX, PPTX, XLSX, PDF, TXT, MD, SRT, VTT). The job runs immediately and returns a FREE preview of the first pages plus a secure Stripe checkout link — the full translated file unlocks after payment ($4.99, one-time, no account needed). Provide EITHER source_url OR base64_content, plus a filename with extension.

Describe what you want done to a file in plain language — e.g. "translate this contract to German", "pull every table out of this PDF into Excel", "shrink this video to under 25MB", "convert this to PDF". The instruction is routed to the right job automatically; if the request is not supported yet you get an honest explanation of what is. Provide EITHER source_url OR base64_content, plus a filename with extension.

Check the status of a previously submitted file job (translate_file, extract_tables, compress_file). Returns the current status, preview + checkout link for completed paid jobs, or the download URL for completed free/paid-and-unlocked jobs.

Translate a PowerPoint (.pptx) deck preserving all formatting. $0.02/slide. Supports 32 languages (Latin, Cyrillic, Greek scripts). Provide job_id (from a previous create_slide/create_deck), pptx_url, or pptx_base64.

Upload assets for PowerPoint (.pptx) generation: company template, logo, image, or document — or AI-generate an image. Purposes: • logo — company logo for chrome (PNG/JPG/SVG, max 5MB) → logo_id • image — image for the Image component (max 10MB) → asset_id • theme — company template PPTX → theme_id; slides with it render NATIVELY on the template (masters/layouts/chrome) • generate_image — AI-generate via `prompt` → asset_id ($0.05) • translate — PPTX to translate → deck job_id ($0.02/slide; requires `target_language`) • pdf — PDF → editable slides; pass `target_language` to also translate • recreate — image OF a slide → editable PPTX slide ($0.10; honest annotate/preserve fallback, refusals free). Use `image` to just place a picture Files >3MB (pdf/translate/theme) — and recreate on chat hosts — omit `data`: a drop-zone appears in the result card; bytes never pass through the agent.

Fetch Chainstack's public pricing and return a normalized snapshot. Use this to answer pricing questions before quoting the user: plan fit, overage math, per-chain dedicated-node costs, and add-on pricing (Unlimited Node flat-fee tiers, Yellowstone gRPC streams, Warp transactions, dedicated-node base rates). This tool returns the menu, not the bill — the calling agent does the arithmetic. All prices are list prices in USD; disclaimers are surfaced in the `disclaimers` field. Design: we pass pricing.md through as raw markdown. Marketing owns that file and its structure changes freely; parsing it server-side would couple us to heading text and table column names we don't control. The LLM reads markdown natively, so handing the raw text to the agent keeps us correct regardless of how the page is restructured. pricing_current.json is parsed into `dedicated_catalog` because it has a stable engineering-owned schema, and the catalog benefits from filtering (to user-orderable SKUs only), unit conversion (cents → USD, milli-cores → cores), and region humanization (via `region_legend`). Per-method RU billing rules are NOT in these sources. Plan-level rates (Full Node = 1 RU, Archive Node = 2 RU) are in the markdown, but some EVM archive-state methods (eth_getBalance, eth_call, eth_getProof, eth_getStorageAt, eth_getCode, eth_getTransactionCount, eth_callMany, eth_createAccessList) and all debug_* / trace_* methods are billed at 2 RU on a full node when called against old blocks. For method-level detail, call `search_docs` with "request units" or `get_doc_page("docs/request-units")`. No API key required — sources are fully public. Each call fetches both sources fresh (no caching), so a stale result isn't possible. Returns: A dict with fields: - `pricing_markdown`: raw markdown from chainstack.com/pricing.md. Read this for plan tiers, feature matrix, add-on pricing, support levels, PAYG details, and provider comparisons. - `dedicated_catalog`: user-orderable per-chain dedicated-node SKUs with flavor, regions (as infra slugs like "sgp1"), hourly and monthly prices in USD. Already filtered to the ~87 orderable SKUs and unit-converted. - `region_legend`: slug → human city name map covering every region slug that appears in `dedicated_catalog`. Use `region_legend[slug]` to translate for display; `regions` keeps the slug as the canonical identifier. - `disclaimers`: list-price caveats (Enterprise "from" pricing, etc.). - `sources`: URL + ok/error per source; the JSON source carries its own `updated_at`. - `warnings`: populated when a source is unreachable or the JSON parser failed. The tool still returns best-effort results. - `fetched_at`: UTC timestamp of this call.

Create a shareable Word Aligner diagram that shows which words match across two or more stacked lines of text (a translation and its source, an interlinear gloss, IPA, etc.). Returns a URL that opens the interactive diagram, plus a preview image. Use this when the user wants to translate a phrase and show word correspondences, align a translation with its source (including RTL scripts like Hebrew or Arabic, or vertically written ones like Japanese and Mongolian), or build a Leipzig-style interlinear gloss. Word indices are 0-based token positions. Tokenize each line the same way the tool does before assigning indices: - Whitespace always splits ("I have been going" -> I[0] have[1] been[2] going[3]). - The characters in settings.tokenSplitChars (default ".-|") also split and are then removed from the rendered text, so "go.PST.IPFV" becomes three tokens (go, PST, IPFV) and the dots disappear. For Leipzig glosses set tokenSplitChars to "-|" to keep the dots. - Punctuation stays attached by default ("Hello, world!" -> Hello,[0] world![1]). - In RTL lines, word 0 is the logically first word (rightmost on screen); index in reading order. - Japanese and Chinese are written without spaces and nothing is segmented for you: put spaces where the alignment units should be. For a vertically written script set settings.axis to "columns". Every line then becomes a vertical column and the connectors run sideways. Set orientation per line: "vertical" stacks the characters (Japanese, Chinese), "sideways" rotates the line a quarter turn (traditional Mongolian, and Latin runs inside vertical text), "upright" leaves a translation as horizontal word boxes. The first line is the leftmost column, so for Japanese and Chinese, whose columns read right to left, list the translation first and the script second. Each alignment is [lineA, wordA, lineB, wordB]; the two lines must be neighbours in the stack (|lineA - lineB| = 1), which means one above the other in rows and side by side in columns. To express many-to-one, list each target word as its own tuple. Tokens that share a connection group get the same color automatically.

Search the Charity Commission register of England and Wales by name or keyword. Returns matching charities with registration number, status, and registration date. Use charity_profile for full details once you have the charity number. The upstream `searchCharityName` endpoint returns the full list in one shot — pagination is applied client-side via offset/limit. A query that matches nothing is a successful empty result (`charities: []`), not an error — the upstream endpoint signals "no matches" with an HTTP 404, which is translated back into an empty result here rather than surfaced as a not-found failure.

Anchor agentic tool for Cat-12. Translate AML/BSA program controls, TM rules, and customer risk policy into a structured Policy Mandate JSON for agentic payment sy Browser-based, client-side only. Zero PII. Link users to https://ainumbers.co/tools/131-ap2-aml-mandate-builder.html for interactive use. Renders the interactive AINumbers tool as a widget; inputs are applied via the AIN Bridge and the tool runs client-side (zero PII, zero network).

Translate a plain-language question into a candidate SQL query using pattern-matching against the live schema (no AI model — simple questions only: counts, averages, filtered selects on a named table). Returns the SQL without executing it, with a confidence score; low confidence means the table was guessed. Review the statement and tables_used, then run it with scalix_db_query. For complex questions, read scalix_db_schema and write the SQL directly.

Find the right ReefAPI engine for a task — pass ENGLISH keywords or a short natural-language use-case ("detect a website's tech stack", "company reviews", "check a package for vulnerabilities", "is this domain available"). The catalog is in English: if the end-user asked in another language, translate their INTENT into English keywords first (you are an LLM — do this inline). Ranks engines by how well the query matches each engine's name/title/category/ACTION descriptions (stem-matched, so plurals/word-forms still hit). Empty query = list all. Returns name/title/category/actions + match score. Call this FIRST, then get_engine_schema(engine) to pick an action. This is a fast keyword pre-filter — if the right engine isn't in the results (or you want to be sure), call get_catalog and pick from the full list YOURSELF (you semantically match any language/phrasing better than keywords).

Look up the geologic time scale — eons, eras, periods, epochs, and ages with their absolute-age boundaries in millions of years (Ma) and nesting. This is the reference that grounds every temporal filter on the other tools and translates a named interval like "Late Cretaceous" to its 100.5–66.0 Ma boundaries (and back). Filter by a name substring, a Ma range (overlap match), and/or a level; call with no filters to browse the full scale. Browsing and every name on the bundled ICS international-scale snapshot are answered offline. A name the snapshot does not carry — the sub-stage and regional names that occurrence and collection rows report, such as "Late Maastrichtian" or "Lancian" — costs one PBDB lookup across its other time scales; the response names which source answered and which scale the interval belongs to.

Register this agent on Agent Guild so others can find and vouch for you. Free, and you only need to do it once. Returns {id, did, api_key, capabilities, next_step}. SAVE the api_key — it is secret and signs every attestation you write. Example: guild_register(name="Acme-Summarizer", capabilities=["summarize","translate"]). If an offer led you here, pass its tag as `src` (e.g. src="passport_offer:mcp") so the follow is attributable. Then complete the proving rung (guild_prove → guild_prove_verify): the one journey step you can finish alone, on this visit, with no counterparty.

Real-time solar wind measurements from the active spacecraft at L1: proton speed (km/s), density (n/cm³), temperature (K), and the critical Bz component (southward Bz = negative = storm driver). Returns the recent plasma and magnetic field time series within the requested window, oldest first, each record tagged with the reporting spacecraft. Bz < −10 nT for sustained periods is a primary geomagnetic storm trigger — use alongside noaa_spaceweather_get_kp_index to see whether elevated solar wind has translated into a geomagnetic storm.

Convert a bank statement PDF into structured data or a spreadsheet. When the user attaches a PDF in the conversation, it arrives automatically as pdf_file — never encode it yourself. Otherwise, pass pdf_url for a public HTTPS link. If your host has no way to reference the attached file at all (no pdf_file/pdf_url equivalent), call request_upload first and pass its upload_id here instead. The base64 pdf parameter is a last resort only, for a caller with no other way to reference the file. To convert several statements in one call, pass upload_ids (the array from a single request_upload call made with count set) instead of pdf/pdf_url/pdf_file/upload_id — mutually exclusive with those four. This batch form only ADMITS each file (queues it, or reports an already-completed duplicate) and returns immediately with a compact per-file status list plus a summary — it never waits for conversion, so call get_statement per document_id once ready rather than expecting inline results here. Returns accounts, transactions, and metadata. output_format "json" (default) returns the data inline, renderable in chat. The other formats (csv, xlsx, qbo, xero) return a time-limited download link instead: present it as a normal link. Every response includes a "summary" field: use it as the single source of truth for what happened. If the conversation is not in English, translate it faithfully into the conversation language; never add details it doesn't contain. Never echo raw status values (e.g. "completed") or field names. Consumes credits (1 per page). Page limit depends on your plan.

Fetch the full converted data for a previously processed document. Use this after convert_statement returns a "processing" status, or to re-fetch results. output_format "json" (default) returns the data inline, renderable in chat. The other formats (csv, xlsx, qbo, xero) return a time-limited download link instead: present it as a normal link. data_mode selects which projection of the data you get: omit it for each output_format's existing default behavior. "normalized" is the cleaned, interpreted view; "original" includes each transaction's raw column values exactly as printed on the source PDF (originalData); "enhanced" is a reformatted view of the original columns (csv/xlsx only for now). Fetch data_mode: "original" when you plan to submit results to evaluate_benchmark — pass its originalData through verbatim; an absent originalData scores that benchmark's raw-fidelity dimension 0 for this document. Every response includes a "summary" field: use it as the single source of truth for what happened. If the conversation is not in English, translate it faithfully into the conversation language; never add details it doesn't contain. Never echo raw status values (e.g. "completed") or field names.

Translate identifiers across databases via UniProt's ID-mapping service — gene names to accessions, accession to PDB / Ensembl / RefSeq / ChEMBL / GeneID, and back. The job runs asynchronously; this tool submits it and polls within a budget. A running job returns status "running" with a ticket; pass that ticket alone to poll the same job. A completed call returns status "finished" with one results page; when continuation is present, pass it alone to fetch the next completed page without re-submitting or polling the job. A gene name often maps to one reviewed Swiss-Prot accession plus dozens of unreviewed TrEMBL ones, so target UniProtKB-Swiss-Prot (reviewed only) for the usual intent, or UniProtKB / UniProtKB_AC-ID to include TrEMBL. Pair a gene-symbol from_db with tax_id to disambiguate species. Chain the resulting accessions into uniprot_get_entry.

Create a Lightning invoice to pay for one AI service call. Returns JSON: { paymentId, invoice (BOLT11), amount (sats), expiresAt }. Each payment covers exactly one tool call — call this once per operation. Typical flow: list_models → create_payment → check_payment_status → call tool. The invoice expires in 10 minutes. Call list_models first to discover modelId values. modelId is optional — omit it to use the default (best) model. Some tools require extra params at payment time because pricing depends on them: generate_text requires prompt (price = f(char count)); text_to_speech requires text (price = f(char count) by tier); transcribe_audio / transcribe_translate take durationMinutes (10 sats/min — declare your audio length, default 1); send_sms, place_call, ai_call require phoneNumber; generate_video and animate_image require duration, and take an optional resolution (250-400 sats/sec by resolution — quote with the SAME duration and resolution you will execute with); edit_image requires resolution (1K=200, 2K=300, 4K=450 sats); epub_to_audiobook requires characterCount (total text characters in the book — price is per-character by voice tier, minimum 500 sats). If required params are missing, the response includes an error with the missing field names.

Compound endpoint — one payment turns audio in any of 13 source languages into both a transcript AND a translation in any of 119 target languages. Perfect for WhatsApp voice messages in a language you don't speak (Yoruba → English), or recording a meeting in another language and reading it in yours. Auto-detects source if omitted. Async — returns requestId, poll with check_job_status(jobType='transcribe-translate'). Flat price covers STT + translation. Cheaper than calling transcribe_audio + translate_text separately for typical voice messages. Pay with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='transcribe_translate'.

Translate a whole EPUB into another language and get an EPUB back. Every chapter is translated with its markup intact — headings, emphasis, footnote links, images and code stay where they were — the package language is retargeted and the table of contents is translated. 119 target languages; the target language picks the engine and the engine's row carries the price (same per-character rate as translate_text for that language, min 50 sats). Async — returns requestId, poll with check_job_status(jobType='translate-epub'), then get_job_result for the download url (temporary, 6h). Pay with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='translate_epub', characterCount (visible characters of the book) and targetLanguage; the real file is re-priced at execution and a short-pay is refunded with the exact amount to re-pay.

Translate text across 119 languages with high accuracy. The target language picks the engine: GPT-OSS 120B by default, or a higher-scoring model (Gemini) where one measurably beats it. Auto-detects source language. Privacy-preserving: no data stored. Pricing: 1 sat per 1,000 characters on the standard engine, minimum 1 sat per request; a routed language costs more. GET /api/languages returns the exact price, engine and measured chrF for every language, and the 402 always quotes the real amount before you pay. Language parameters accept English names ('Spanish', 'Chinese (Simplified)') or ISO-639 codes / locale tags ('es', 'en-US', 'pt-BR', 'zh-Hans'). Supported languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Basque, Belarusian, Bengali, Bosnian, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese (Simplified), Chinese (Traditional), Corsican, Croatian, Czech, Danish, Dari, Dutch, English, Esperanto, Estonian, Farsi, Fijian, Filipino, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Kinyarwanda, Korean, Kurdish, Kyrgyz, Lao, Latvian, Lingala, Lithuanian, Luganda, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Occitan, Odia, Pashto, Polish, Portuguese, Punjabi, Romanian, Romansh, Russian, Samoan, Scots Gaelic, Serbian, Sesotho, Setswana, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Tatar, Telugu, Thai, Tigrinya, Tongan, Turkish, Turkmen, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, Wolof, Xhosa, Yiddish, Yoruba, Zulu. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='translate_text' and prompt (the text to translate).

Ask a frontier AI a question in your OWN language and get the answer back in that same language, in ONE payment. We translate the question to English, answer it with a frontier model, then translate the answer back. Two engines behind one endpoint: 119 mainstream languages use the LLM translate tier; anything those models do not serve falls through to MADLAD-400 on our own GPU (452 languages, 251 of which ChatGPT, Claude and Gemini do not support at all). So a Wolaytta, Tiv or Q'eqchi' speaker gets frontier reasoning in their own language, which no other API offers. Rare-language quality is uneven and published per language - GET https://sats4ai.com/api/languages. Priced from question length plus a fixed 2000-character answer allowance at the chosen tier's rate; the rare-language path costs more (GPU both ways). Requires create_payment with toolName='multilingual_ask' AND the `language` you intend to use - the quote depends on it.

Translate into 452 languages, 251 of them NOT supported by ChatGPT, Claude or Gemini (29 of those 251 measured at fair quality or better against human references) — including Bhojpuri (~50M speakers), Maithili (~34M), Egyptian Arabic (~100M), Moroccan Arabic (~30M), Chhattisgarhi, Magahi, Manipuri, Kashmiri, Shan, Kachin, Awadhi, Tamazight, Crimean Tatar, Quechua, Nuer, Sango, plus indigenous and minority languages with no callable API anywhere. Runs MADLAD-400 (Apache-2.0). QUALITY VARIES AND IS PUBLISHED PER LANGUAGE: every language carries a measured tier — good (chrF++ >= 45 vs human reference translations), fair (32-45), unverified (no benchmark exists, untested, may be poor), experimental (known weak). The response repeats the tier so you can judge how much to trust it. GET https://sats4ai.com/api/l402/translate-rare-language for the full language list with tiers, or GET /api/languages. Unsupported languages are rejected BEFORE payment. For mainstream languages use translate_text instead — it is cheaper and more fluent. Priced 50 sats base + 0.002 sats/char (GPU). Pay with Bitcoin Lightning — no API key or signup. Requires create_payment with toolName='translate_rare_language'.

Get everything needed to make a monitor actually report: the ping URL, copy-paste check-in snippets, and the three MECHANISMS for reporting, returned together. Call this right after create_monitor. CHOOSE BY WHAT THE MONITORED THING IS — read `reporting_options` first and pick by that, rather than defaulting to the raw curl list: `how_to` — the manual protocol — is the UNIVERSAL path: it works in any agent, any language, any tool, with no prerequisite, so it is the default choice for any agent this applies to. Pair it with expect_every_s (the silence floor, set via update_monitor) so an agent that quietly stops reporting opens a detected incident instead of leaving its monitor reading healthy. If you ARE Claude Code specifically, `hook_install` is available as an OPTIONAL SHORTCUT, not a better tier: a one-time install that binds reporting to Claude Code's own hooks (UserPromptSubmit, Stop, StopFailure), automating how_to's exact same protocol so reporting becomes a property of your event loop instead of something you must remember — and it is the only mechanism that can send every state this product models, including blocked and note. hook_install is Claude Code specific: if you are a DIFFERENT AI agent — even one with its own hook or event system, Cursor, Windsurf, Codex, a custom framework — do NOT translate its steps into your own hooks; the event semantics differ and a translated install can pass its own verification while never reporting, so use `how_to` instead. If what you are monitoring is launched as a command instead — a cron job, a CI step, a script, or an agent started from a shell — use `run_wrapper`: wrap the command with `lastping run` and a separate process reports for you, so nothing has to be remembered; the tradeoff is that it reports the process's own lifecycle (start, success, fail, cancel) and has no way to send blocked or note. Whichever you choose, the underlying protocol is the same: the success ping at the END of the work, the fail URL if it failed, the start ping first for long or possibly-hung runs (this enables overrun / never-finished detection), and a step (curl_step) as each stage completes so a run that wedges mid-way is caught by name rather than only when its whole budget expires. Also read `expectations_how_to`: before you start work, use declare_run_expectations to say how THIS run should be judged when it closes — a one-time, unchangeable commitment that replaces the run grading itself. And `discovery_how_to`, which is about the OTHER jobs on this host or in this repo: how to find the scheduled work nobody is watching yet and propose it, rather than monitoring only the one thing you were asked about.

Resolve an ISO 639-1 language code such as en, fr, or ja to its English name and native name when you need language metadata from a two-letter code. Use when: - What language does ISO 639-1 code ja refer to? - Get the native name for language code fr - Resolve a two-letter language code to its English and native names Do not use when: - Translate text between languages - Detect the language of arbitrary free-form text - Look up country languages from a country code (use country_lookup)

Check Python source without running it: parse, lint (ruff), type-check (mypy), AST security policy, credential scan. Safe on code you do not trust. Use it on every Python file you generated or edited, before writing it to disk. Alternatives: repair_python to get the corrected source instead of the diagnosis; execute_python to prove the code runs. Auth: a key is required. A free key covers this call, 25 per day, then HTTP 429; get one with POST /v1/keys. Credits are bought without an account, 1 per call: GET /v1/pricing says where to send the xDAI. Or pay for this one call with no key at all: call it without one and the result carries x402 payment requirements ($0.01 in USD Coin on eip155:8453); sign them and repeat the call with the payment in _meta['x402/payment']. Arguments: code: the whole file, 1..200000 bytes of UTF-8 measured after encoding (empty is refused with 400, larger with 413); a fragment is fine, but line and column numbers in the answer count from 1 in what you sent. language: must be 'python'; anything else is 400, and the field may be omitted. Of options only transpile_to (e.g. 'javascript', which returns a translated copy in transpiled) acts here; timeout_s, max_iterations, optimize, examples and expected_output need a pass that rewrites or runs the code, so send code alone. Ignored options are not refused, so a call that sets them looks like it worked; and code that does not parse is answered rather than refused: valid=false with the syntax error located, which is the point. Returns valid, score 0..1, diagnostics (rule, message, line, column), security findings, fixes, fixed_code and runtime; see outputSchema. The code and its verdict are retained to improve the service.

Translate a matchup (home team + away team) into the eventId needed by get_game_props. Read-only. No side effects. Requires an API key; rate-limited per your tier. Use this when you know the teams playing but don't have the eventId. On success returns: { eventId }. Pass that id straight to get_game_props. On failure returns an error explaining that the game was not found on today's board. If multiple games match the team names (rare), returns the first match sorted by start time. Matching is case-insensitive substring containment against the full team name. When not to use: use list_games to browse a slate, or get_game_props directly if you already have the eventId.

Evaluate the Apier Rulebook for a Norwegian organisation and return every applicable regulatory obligation with its current state and legal reference. One entry per obligation: a stable obligation_id (e.g. `MVA_FILING_BIMONTHLY`), the lovdata legal_reference, the state enum (`filed` / `pending` / `in_progress` / `failed` / `overdue` / `unknown`), the bokmål description inherited byte-for-byte from the Rulebook (never re-translate it), and the freshness window. Deterministic, always against the current instant (no as_of at v1). Choose this for the full obligation menu. Failure modes: NOT_FOUND, SCOPE_INSUFFICIENT (needs read:brreg), VALIDATION_FAILED. For the calendar alone, use get_company_deadlines instead; for the entity-type-level set needing no organisasjonsnummer or key, use get_public_obligations instead. No API key? Bearer apier_sandbox_test_<suffix> (fresh suffix) serves synthetic fixtures; org numbers: GET /api/v1/sandbox/fixtures. Cost: 50 øre (NOK 0.50) per call, prepaid (prices: the get_pricing tool or GET /api/v1/pricing; a shortfall returns INSUFFICIENT_CREDITS with top_up_url). Docs: https://www.apier.no/docs/guides/norwegian-company-obligations

List the Analytics Legends deep-research studies with their edition, as-of date, audience, word count and canonical URL. METADATA ONLY: study bodies are a paid Consultant-tier deliverable, served by `get_study` on this same endpoint with a subscriber key. Use this to tell a reader that a study exists and where to read it. ONE ROW IS ONE LANGUAGE EDITION, NOT ONE STUDY: each study is published in every language it has been translated into, so `_meta.tranche_total_row_count` counts editions and `_meta.distinct_studies` counts the works. `_meta.available_languages` gives the live per-language counts; each row carries its `editions` list. Pass `lang` to get one row per study.

IDENTITY CROSSWALK for one symbol — every identifier the security and its issuer carry, which rung matched, and how confident that match is. Returns SECURITY-grain identifiers (CUSIP9, CUSIP8, ISIN, SEDOL, ticker, estimate-vendor ticker) kept deliberately SEPARATE from the ISSUER-grain keys (gvkey, CUSIP6, regulator filer number, entity id), because those identify a company and not a share line; plus the issuer's other listed securities, the dated timeline of every identifier this security has ever been bound to, and the bridge into the private-company graph with that link's confidence grade. Call it to join two data sets that key on different identifiers, to translate a CUSIP-keyed holdings file into tickers, or to find out what a symbol used to be. as_of=YYYY-MM-DD returns the identifiers that were IN FORCE on that date, not today's — FB resolves today to an ETF and Meta's 2012-02-01→2022-06-08 hold on the string comes back as a dated prior binding, never as the answer. A symbol shared by more than one current security is REFUSED with its candidate list rather than guessed; pass prefer_country to choose. Coverage is a number in every response: what THIS answer contains, and the corpus census (77,313 securities / 58,179 issuers / 969,333 identifier bindings / 26,189 issuers linked to the private graph, live-measured 2026-08-02). Identifier-history depth is uneven by construction — 38,850 of 77,313 securities carry a prior ticker — and every source block states its own as-of, its age in days and whether that age is past its expected refresh cadence.

Find AINSOF music from a written brief — mood, scene, genre, energy, instruments. Example: 'lo-fi hip hop underscore, warm, no vocals'. Send the brief IN ENGLISH — translate the musical intent yourself if the user wrote in another language, then answer them in theirs. Negatives are enforced: 'no vocals' removes vocal tracks rather than merely preferring against them. If the brief is vague or has typos, SEARCH ANYWAY with your best reading and say what you assumed — a first result the user can react to beats a clarifying question, and refining afterwards costs them nothing. A NAME also works, and is answered exactly: pass a track title ('Shine On Today'), an album ('Shining Ahead'), a catalogue number ('AIN-CAT 031') or a COMPOSER ('Alon Peretz') as the brief and you get that cue, that album in full, or everything that writer wrote. A composer named inside an ordinary brief puts their cues first without narrowing it. NEVER tell a user we do not have a track until you have passed its name here.