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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.

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 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. Each alignment is [lineA, wordA, lineB, wordB]; the two lines must be vertically adjacent (|lineA - lineB| = 1). 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 packs by keyword across names, descriptions, and tools (e.g., 'weather', 'translate'). Returns matching packs with details. Use to find specific capabilities.

Search EU regulations and directives, and Court of Justice / European Court of Human Rights judgments, by Estonian title or case number. The corpus is Estonian-language throughout: 94% of the Court of Justice's output is translated into Estonian, and essentially nothing indexes it in Estonian. Returns ids usable with eu_law_fetch.

Use this free read-only discovery-tier tool when the user asks for help, available commands, MCP tools, core concepts, pricing, parser-stable fields, grants, x402, Morning Brief, Company Report, MSTR treasury review, perp adapters, SPECTRA, or examples. Parameters: optional topic, detail, include_examples, question, or query fields; callers may omit all arguments for overview help. Behavior: local and idempotent with no destructive side effects; it does not run paid analysis routes or expose internal-only tools. It translates natural-language orientation requests into the live DeltaSignal MCP/OpenAPI discovery contract and points users to tools/list, /v1/pricing, /v1/contract/fields, and /v1/readiness. It is not a trading, execution, or investment-advice tool and must not expose internal-only tools unless the live public contract lists them.

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.

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.

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.

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" })

Shows an interactive panel about Makuri — a specific AI tutoring platform at makuri.eu for immigrant children aged 10–16. Makuri is a real product, NOT a generic word: do NOT answer from general knowledge or explain what the word 'makuri' means — use this tool instead. Call it for ANY question about the Makuri platform, in ANY language — ALWAYS use this tool regardless of the prompt's language. Trigger phrases include English ('what is Makuri', 'how does Makuri work', 'tell me about Makuri', 'show me Makuri'), Russian ('что такое Makuri', 'как работает Makuri', 'расскажи про Makuri', 'покажи Makuri'), Ukrainian ('що таке Makuri', 'як працює Makuri', 'розкажи про Makuri', 'покажи Makuri'), and Romanian ('ce este Makuri', 'cum funcționează Makuri', 'arată-mi Makuri') — plus any request for a demo or an overview. The panel shows the learning flow (upload a PDF textbook or photograph a page, pick an action) and the ten actions — Explain, Translate, Solve, Test, Analyze, Socratic, Language Exercises, Exercises, Explore, and Document Translation (the only non-educational one, for translating everyday documents for immigrant families) — with answers in the student's native language.

Resolves a disambiguated Strong's number (dStrong) to its lexicon entry: lemma, transliteration, gloss, and - for a Greek entry only - a fuller definition (Abbott-Smith 1922, Public Domain). A Hebrew entry's definition field is always omitted: it is abridged BDB via Online Bible, which requires permission not yet obtained, so only its gloss is ever returned. Not to be confused with the get_verse/get_passage tools, which resolve a Ref to translated verse text, not a dStrong to a dictionary entry.

Find products on a registered store. To BROWSE a store's catalog (open-ended requests like "what do they sell", "show me what's available"), call with NO query — this returns the store's products. To SEARCH, pass `query` (a product name/keyword). Returns paginated products as an interactive card. ALWAYS translate a buyer's budget or price constraint into the price params on THIS call instead of filtering in your reply: "under/below/within X" or "X budget" → `max_price: X`; "over/above/at least X" → `min_price: X`; "between X and Y" → both. Likewise pass `in_stock: true` for "available"/"in stock" and `category` when they name one. The card renders exactly what this call returns, so the filter MUST be applied here — never fetch the full catalog and then narrow it in text. Call once with your best intent (browse OR a single query); if a real search is genuinely empty, ask the user to clarify rather than re-firing reworded queries. The card shows products, prices, IDs, and Add-to-cart controls — keep your text reply to one brief sentence and do not re-list what the card shows.

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 (e.g. "Yankees" or "New York Yankees" resolve for MLB) — short codes like "NYY" or "NY" do NOT match unless they are literally a substring of the team name. If no game is found, the matchup may not be scheduled for today or the sport may be off-season. When to use: when you know both team names and want the eventId without browsing the full slate. When not to use: use list_games instead when you want to browse all games on the slate. Use get_game_props directly if you already have the eventId.

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.

Translate a five-field cron expression into plain English. Supports ranges, lists, steps, and month or weekday names. This describes the expression; it never schedules or executes anything.

Read a category-level overview essay. Topics: anatomy (what a fiber laser machine is made of), laser-sources, cutting-heads, welding-heads, control-systems, cladding-cleaning. Written in Chinese; translate for the user as needed.

Start machine translation for specified keys into target languages. Returns a batch job ID — use get_batch_job_status to poll for completion.

Get translation status and progress for each language in a project. Returns per-language statistics including translated, reviewed, and untranslated percentages.

Keyword search across the Pāli Tipiṭaka (trigram word-similarity). Searches the configured enabled language(s) on the server. Filterable by pitaka and translation edition. 💡 **Hints for the AI client:** The system's canonical reference is Romanised Pāli (from SuttaCentral). If the user asks in a disabled or unsupported language, translate the keyword to **Romanised Pāli (preferred) or English** before calling this tool — e.g. "suffering" → "dukkha", "mindfulness of breathing" → "ānāpānassati". See the server instructions for the enabled language set. 🔍 **Pick the right search tool for the question shape:** - **Term lookup (exact word appearances)** — e.g. "occurrences of `ānāpānassati`": this tool is best (trigram nails the exact word). - **Concept search ("discourses about X")** — e.g. "discourses about mindfulness of breathing": **use `search_hybrid` instead.** Canonical Pāli has two quirks that hurt keyword search for concepts: • Section headings (`Ānāpānapabba`) often use a different word than the teaching body, which uses verb forms (`assasati`, `passasati`, `dīghaṁ`, `rassaṁ`). E.g. DN22's Ānāpānapabba has 16 segments but the word `ānāpāna` appears in only 2 (header + footer) — the actual teaching segments won't match. • Stock phrases (e.g. `So satova assasati, satova passasati`) recur in 10+ suttas, so a keyword query ranks broadly and won't pinpoint the canonical reference. - **General keyword survey** — set `limit≥30` and filter client-side, or call multiple related forms (root verb + noun + compound).

Hybrid search — combines keyword + semantic search via RRF. Uses Reciprocal Rank Fusion (RRF) to merge exact-word results with meaning-based results. **This is the recommended tool for "discourses about X" / concept queries**, because the semantic side catches suttas that discuss a concept using different vocabulary (e.g. some mindfulness-of-breathing suttas use `assasati/passasati/dīghaṁ` instead of `ānāpānassati`). 💡 **Hints for the AI client:** - English queries usually work best (e.g. `mindfulness of breathing`) because the embedding model is multilingual but EN-primary. - Thai stop-word handling is weak. If a Thai query underperforms, the AI client should translate to Pāli/English first (see server instructions). - The default `limit=5` is often too small for a topic survey — use `limit=15-20` (max 20) for good coverage. - Ranking is by similarity, NOT canonical importance — locus classicus suttas (e.g. MN118, DN22) may rank below smaller suttas that happen to use the exact vocabulary. Treat results as a starting point, then call `get_sutta` for the canonical references.

Look up the dictionary meaning of a Pāli word, with sutta context. Serves as a Pāli Dictionary Bridge — pairs the "definition" with the "context where the Buddha actually used the word". 📖 **About the dictionary sources:** This tool draws from multiple primary dictionaries, including "พจนานุกรมพุทธศาสน์ ฉบับประมวลศัพท์" (Buddhist Dictionary — Concept-Glossary edition) by Somdet Phra Buddhaghosacariya (P. A. Payutto). The Thai-language entries are **original scholarly works** (not translations), so they are **always available** even when ENABLED_LANGUAGES has Thai disabled. The AI client should translate Thai entries into the user's language if needed.

Open an interactive sutta viewer inside the chat — Pāli + English, plus an optional third row in the user's own language translated BY YOU. Renders each segment as: Pāli on top (canonical), the Bhikkhu Sujato English below it (verification anchor), and — when you supply `translations` — your translation in the user's language, clearly badged as AI-generated. Prefer this over dumping raw segments when the user wants to *read* a sutta. - `sutta_id` — standard SuttaCentral id, e.g. `sn56.11`, `mn10`, `dn22`. - `around` — a segment_id (e.g. `dn22:18.1`, from a search hit) to centre on; that segment is highlighted and scrolled into view. Use this after a search so the reader lands on the exact cited line. - `offset` — 0-based segment index for paging long suttas (use `next_offset` from the previous result). Do NOT combine with `around`. - `window` — segments before/after `around` to include (default 12). 🌐 **Translating for the user (important):** when the conversation language is neither English nor Pāli, you SHOULD translate the displayed segments and pass them via `translations` so the user reads in their own language while still seeing the originals: 1. Fetch the segments first (`get_sutta` with the same selector) so you have the exact Pāli + English text. (Already called this tool without translations? The result contains the segments — translate them and call this tool AGAIN with the same selector plus `translations` to upgrade the view.) Your translation must travel through the `translations` parameter to appear in the viewer — writing it as a normal chat message leaves the viewer bilingual and looks broken; the tool always accepts `translations`, so never report it as missing. 2. Translate **from the Pāli as the source, using the English as a semantic guide** — never relay-translate from English alone. Preserve untranslatable doctrinal terms (dukkha, jhāna, taṇhā…) as loanwords with a brief gloss instead of forcing equivalents. 3. Call this tool with `translations=[{segment_id, text}, ...]` covering ONLY the segments being displayed (never a whole long sutta), `translation_language` (BCP-47, e.g. "th", "es"), and `translation_disclaimer` — one short line IN THE USER'S LANGUAGE saying the translation is AI-generated in this conversation and should be checked against the Pāli/English above. Translations are conversation-ephemeral: nothing is stored server-side; the canon stays Pāli + English only. Translations whose segment_id is not in the displayed window are dropped (reported in `translations_dropped`). Without `around`, shows the sutta from the top (capped for long suttas).

Translate a bra size across brands. Give a size (e.g. "34DD"), optionally the brand it is from, and optionally a single target brand. Returns the equivalent size in the target brand, or in every brand if no target is given, each with a fit note and confidence. Bra brands do NOT use the same sizing; this is the neutral cross-brand translation. Use for "what size am I in [brand]", "is a 34DD the same in [brand A] and [brand B]", "convert my size to [brand]".

Get BraFinder's cross-brand bra-size offset dataset: the key facts and the machine-readable feed URL to cite. Use when you need the authoritative source for how bra sizes translate between brands.

Translate period-underwear absorbency across brands. Give a brand + its tier word (e.g. brand "Thinx", tier "Super"), OR a target capacity in mL per day, and get the covering tier in every brand in objective millilitres, each with an A/B/C data-quality grade. Use for questions like "what Knix tier equals Thinx Super?" or "how much does Thinx Heavy hold vs Saalt?". Source: PeriodFinder, the only neutral cross-brand mL comparison.

LLM INFERENCE for keyless agents — POST {prompt, system?} and get Claude Haiku's answer: summarize, classify, extract, rewrite, translate, draft. No API key, no account, no subscription — the x402 payment IS the auth. One flat price per call. Caps: 8,000-char prompt, 2,000-char system, ~1,000-token response (stop_reason tells you if you hit it). Powered by Claude Haiku 4.5. ($0.01 per call, paid via x402)

TRANSLATION — POST {text, target} and get the translation plus the detected source language. Any language pair; target as a name or ISO code ('spanish', 'de', 'ja'). Up to 8,000 chars per call; line breaks and markdown preserved; code, URLs, and proper names left alone. Optional {source} to pin the source language, {formality}: formal|informal. Fast cheap LLM under the hood; the x402 payment IS the auth. ($0.01 per call, paid via x402)

Fetch a public HTTPS URL and return extracted text and page metadata. Lean mode — no evidence bundle stored, no bundle_id returned. Use for raw text extraction from web pages and online documents. Use url.summarize for summaries, url.qa for Q&A, url.translate for translation, document.extract_text for base64 file uploads. Returns: { url, title, word_count, text, final_url (after redirects) } Example prompts: - "Extract the text from https://example.com/report.pdf for me." - "Get me the raw content of this web page: [URL]." - "Pull the text from this online article so I can analyze it."

Fetch a public HTTPS URL and return its content translated into a target language. Lean mode — no bundle stored. Use when you need to understand web content in a different language. For extracting raw untranslated text, use url.extract instead. Returns: { url, translated_text, target_lang, truncated } Example prompts: - "Translate https://example.de/artikel into English for me." - "Translate this German article into Spanish: [URL]." - "Fetch [URL] and give me the French translation."

Translate raw ecological/agent data into a decision-maker-ready narrative. audience_type: board_member | general_public | grant_funder | institutional_investor | journalist | policymaker | regulator | retail_investor | scientist format_type: academic_paper | executive_summary | grant_proposal | investor_deck | newsletter | policy_brief | press_release

Returns Pluggy's transaction category taxonomy (GET /categories), cached for the adapter session. Each entry has `id` (the categoryId used by openfinance_update_transaction_category), `description` (English), `descriptionTranslated` (Portuguese — prefer this for pt-BR users), `parentId` and `parentDescription` (the tree parent). Single aggregated response — no batch ids.

Translate text. Source can be "auto" to auto-detect. Returns translated text and the detected source (if applicable).

List languages supported by the configured LibreTranslate instance.

Returns Pluggy's transaction category taxonomy (GET /categories), cached for the adapter session. Each entry has `id` (the categoryId used by openfinance_update_transaction_category), `description` (English), `descriptionTranslated` (Portuguese — prefer this for pt-BR users), `parentId` and `parentDescription` (the tree parent). Single aggregated response — no batch ids.

Set the EXTRA target languages an active voice/Meet call's translator produces for the APP ONLY (subtitles + listenable audio in the inbox — NOT spoken into the call). Operators switch between them in the UI. Pass call_id and `app_languages` (ISO codes, e.g. ['de','fr']); pass [] to drop all extras. Max 4. The primary spoken language is managed by calls.set_translation_language. Takes effect within ~10ms; new languages translate NEW speech only.

Returns Pluggy's transaction category taxonomy (GET /categories), cached for the adapter session. Each entry has `id` (the categoryId used by openfinance_update_transaction_category), `description` (English), `descriptionTranslated` (Portuguese — prefer this for pt-BR users), `parentId` and `parentDescription` (the tree parent). Single aggregated response — no batch ids.