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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}.

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.

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.

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.

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.

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).

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.

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.

Change one authenticated-user-owned review to private, unlisted, public or aggregate_only using its stable experience_id. Use a preceding list_reviews_by_visibility result to translate conversational list numbers back to stable IDs. Setting public also ensures publication_status=published.

Use this when you need to translate text from one language to another. Supports 50+ languages with automatic source language detection. Returns translated text, detected source language, and confidence score. Ideal for multilingual content, localization, and cross-language communication. Do NOT use for summarization — use ai_summarize_text. Do NOT use for sentiment — use text_analyze_sentiment.

Use this when you need to identify what language a text is written in. Uses n-gram frequency analysis to detect 30+ languages with confidence scores. Returns top 3 language matches, script detection (Latin, Cyrillic, Arabic, CJK, Devanagari), and character statistics. Ideal for multilingual content routing, pre-translation detection, and content filtering. Do NOT use for translation — use text_translate. Do NOT use for sentiment — use text_analyze_sentiment.

Use this when you need to convert text to speech audio. Returns base64-encoded MP3 audio in JSON. Returns: 1. audio (base64 MP3 data) 2. language used 3. textLength (character count) 4. durationEstimate in seconds 5. format (mp3). Example output: {"audio":"SUQzBAAAAAAAI1RTU0UAAAAP...","language":"en","textLength":45,"durationEstimate":3.2,"format":"mp3"} Use this FOR generating audio narration, building voice assistants, creating audio versions of articles, accessibility features, and language learning apps. Do NOT use for language detection -- use text_detect_language instead. Do NOT use for text translation -- use text_translate instead. Do NOT use for OCR from images -- use media_extract_text_from_image instead.

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.

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.

Render a click-driving YOUTUBE / Shorts / Instagram THUMBNAIL or video cover — the full production pipeline (concept framework → casting → scene → render → surgical tweaks → text), not a bare image prompt. Use this for any "thumbnail", "video cover", "video preview" or MrBeast-style packaging ask INSTEAD of generate_image. About 9 credits per variant; the headline overlay is free. CONCEPT — every thumbnail must open an INFORMATION GAP (the image raises a question the title answers) while staying truthful to the video. Brainstorm ≥5 concepts across the 16 frameworks before you pick, and feel free to combine two. Frameworks (pass as `framework`): before_after · social_ui · three_step · screenshot · posed_portrait (the default) · posed_action · specific_day · graphical · landscape · map_aerial · product · adding_text · repetition · size_difference · news_clip · amplified_reality. Call hermoso_capabilities for each one's full 'realize it with' note plus the emotion, overlay-style, font and rim-colour catalogs. THREE GATES, all BEFORE you render: 1. WHO IS IN FRAME — never assume and never silently substitute a stranger. If the framework puts a person in frame and no face photo is attached, the tool refuses (nothing rendered, nothing charged) and tells you to ask the user once: themselves (send a face photo → the identity gets locked), a generated person (`castGenericPerson:true`), or a people-free framework. 2. TEXT — the default is a CLEAN render with the headline TYPESET OVER THE TOP afterwards (free, always legible, correctly spelled). Just pass `headline`. Only set `bakeText:true` if the user explicitly asks for the words painted INTO the image — verified live, that renders the asked-for words correctly but leaks garbled invented text across the rest of the frame. Never infer text intent from the topic or the framework. 3. HOW MANY — ask once whether they want one thumbnail or a SET (offer 4: the same concept at different emotions and/or camera takes). Default is 1; `variants` caps at 16. IDENTITY LOCK is automatic for every attached face photo. `emotion` is the single biggest CTR lever on a face: shock · hype · fear · confusion · determination · smug · charisma · disgust · awe · rage · laugh (or your own phrase). Finished thumbnail needs a fix? Re-call with `tweak` + `sourceImage` for a surgical, pixel-faithful edit (emotion / background / background_color / rim_light) instead of re-rendering — tweaks chain. ALWAYS check the returned postRenderCheck against the image before you present it. PROMPT LANGUAGE — write every DESCRIPTIVE field in ENGLISH (`sceneBrief`, `keyElements`, `location`, `composition`, `background`, `topic`, each person's `describe`, and every `reference` field), translating the user's wording where needed: the image models are trained on English and a non-English scene description renders noticeably worse. Text that gets BAKED OR TYPESET stays verbatim in the user's own language — `headline`, `headlineLines` and `bakedUiText` are never translated.

Localize a finished video into another language WITHOUT re-rendering it: the spoken track is transcribed, translated, re-voiced and lip-synced back onto the SAME footage, so the visuals, timing and edit are untouched. Just pass the video and the language — the script is read off the source automatically (pass `script` only to override what it heard). Paid; returns the served URL of the localized video.

Live SEPTA bus and trolley vehicle positions for a route in Philadelphia — each vehicle with direction, destination, next stop, minutes late, estimated seat availability (crowding), and lat/lon. Buses use route numbers ("23", "47"); trolleys use SEPTA Metro codes T1-T5, G1 (Girard), D1/D2 (Media/Sharon Hill) — legacy trolley numbers like "10" are auto-translated to T1. Example: septa_bus_positions({ route: "23" })

Translate text into a supported Poly-Glot language. Preserves meaning, formatting, names, code, and URLs.