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305,084 tools. Last updated 2026-07-22 18:15

"Hugging Face" matching MCP tools:

  • Get details for one or more Hugging Face repos (model, dataset, or space). Auto-detects type unless specified. For datasets, use operations: overview, dataset_structure, dataset_preview. Use dataset_structure first to discover configs, splits, sizes, and schema. Use dataset_preview only when config and split are known, unless the dataset has a single config/split.
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  • Hugging Face tools are being used anonymously and may be rate limited. Call this tool for instructions on joining and authenticating.
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  • Search Hugging Face repositories with a shared query interface. You can target models, datasets, spaces, or aggregate across multiple repo types in one call. Use space_search for semantic-first discovery of Spaces. Include links to repositories in your response.
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  • Routes a prompt to the best available LLM. Two backends: 1. DEFAULT — Hugging Face (Qwen2.5-7B, free with API key) 2. PREMIUM — OpenVecta (GLM-5.2 and more, set provider:'openvecta') Use ONLY when you need external LLM help. Never for things you can answer from context. Returns: { response: string, model: string, provider: string, tokens_used?: number }
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  • Lists all workouts in a date range — compact overview with type, duration, distance, pace, and heart rate. Use this tool first for an overview. For details on a single workout, use get_workout_detail. The workout ID in the output can be used with get_workout_detail and get_workout_samples. Parameters: - start_date: Start date in YYYY-MM-DD format - end_date: End date in YYYY-MM-DD format - activity_type: Optional. Filter: 'RUNNING', 'CYCLING', 'STRENGTH_TRAINING', etc. Matches all type-aliases — 'CYCLING' also returns ROAD_BIKING / MOUNTAIN_BIKING / INDOOR_CYCLING etc. - prefer_provider: Optional per-query override (e.g. 'WHOOP', 'GARMIN'). For each duplicate-cluster, the row from this provider wins (if present). Clusters without this provider remain on the default picker — no data is lost.
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  • Calculate a complete Western natal chart using the tropical zodiac and Swiss Ephemeris. Returns 10 planet positions with Placidus (or chosen) house placements, essential dignities, all active aspects, and element/modality/hemisphere balance statistics. SECTION: WHAT THIS TOOL COVERS Tropical natal chart: Sun, Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto. Each planet returns tropical longitude, sign, house (1–12), retrograde flag, dignity label (domicile/exaltation/detriment/fall/peregrine), dignity score (domicile +5, exaltation +4, triplicity +3, term +2, face +1, detriment -5, fall -4), is_exaltation_degree (within 1° of exact exaltation), dignity_disputed (true for outer planets where exaltation/fall is disputed among modern astrologers). Aspect orbs: conjunction/opposition 5°, square/trine 5°, sextile 3°, minor aspects 1.5°. Not Vedic sidereal (asterwise_get_natal_chart). SECTION: WORKFLOW BEFORE: None — this tool is standalone. AFTER: asterwise_get_western_transits_daily — layer current transits over this natal chart. AFTER: asterwise_get_western_synastry — compare this chart against a partner's chart. AFTER: asterwise_get_western_solar_return — annual return chart for the current year. SECTION: INPUT CONTRACT birth.date — YYYY-MM-DD. Example: '1985-11-12' birth.time — HH:MM (24-hour local time). Example: '06:45' birth.lat — Decimal degrees, north positive. Example: 19.076 (Mumbai) birth.lon — Decimal degrees, east positive. Example: 72.8777 (Mumbai) birth.timezone — IANA timezone string. Example: 'Asia/Kolkata', 'America/New_York', 'Europe/Rome', 'UTC'. Default: UTC. IMPORTANT: Timezone defaults to UTC — always supply the correct local timezone for accurate house cusps. An incorrect timezone shifts the Ascendant. birth.house_system — 'placidus' (default, most common), 'koch', 'equal', 'whole_sign'. Placidus is standard for most Western traditions. Whole sign is traditional/Hellenistic. NOTE: house_system is accepted here but silently ignored by transit, return, synastry, composite, and progression endpoints — those always use the birth location coordinates without house-system selection. ayanamsa — always tropical regardless of any value supplied; field is not present. SECTION: OUTPUT CONTRACT data.zodiac (string — 'tropical') data.house_system (string — the system used) data.ascendant — { longitude (float), sign (string), sign_index (int 0–11), degree_in_sign (float) } data.mc — same shape as ascendant data.planets[] — 10 objects (Sun through Pluto): name (string), longitude (float), sign (string), sign_index (int 0–11) degree_in_sign (float), house (int 1–12) is_retrograde (bool), dignity (string), dignity_score (int) is_exaltation_degree (bool), dignity_disputed (bool) data.houses[] — 12 objects: house (int 1–12), cusp_longitude (float), sign (string) sign_index (int 0–11), degree_in_sign (float) data.aspects[] — each: planet_a (string), planet_b (string), type (string) exact_angle (float), orb (float), is_applying (bool) data.elements — { fire (int), earth (int), air (int), water (int), dominant (string) } data.modalities — { cardinal (int), fixed (int), mutable (int), dominant (string) } data.hemisphere — { eastern (int), western (int), northern (int), southern (int) } data.ayanamsa_value (float — 0.0 for tropical) data.ayanamsa_used (string — 'tropical') data.birth_time_provided (bool) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable natal report. Both modes return identical underlying data. SECTION: COMPUTE CLASS MEDIUM_COMPUTE (~300ms) SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): — WesternBirthData Pydantic violations (date pattern, time pattern, lat/lon bounds) → MCP INVALID_PARAMS INVALID_PARAMS (upstream): — None expected for valid coordinates and dates post-1800. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Polar latitudes (above ~65°N or below ~65°S) may cause Placidus house calculation failure; use whole_sign or equal house system for polar births. — time='00:00' accepted; lagna-sensitive results are unreliable for unknown birth times. SECTION: DO NOT CONFUSE WITH asterwise_get_natal_chart — Vedic sidereal chart using Lahiri ayanamsa; different zodiac, different house system, different planet set (9 grahas vs 10 tropical planets). asterwise_get_western_aspects — takes raw longitudes as input; use when you already have positions and don't need full chart computation.
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    Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.
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  • AceDataCloud Face Transform MCP: keypoints, beautify, age/gender, swap, cartoon, liveness

  • Connect to Hugging Face Hub and thousands of Gradio AI Applications

  • Cut a 9:16 vertical clip from any prior video job (find_clips, summarize, or video transcribe), suitable for direct upload to TikTok, Instagram Reels, or YouTube Shorts. Default output is 1080×1920 H.264 / AAC `.mp4` with center-cropped framing; audio loudness-normalized to -14 LUFS / -1.5 dBTP for short-form social. Single-segment only; clip duration must be between 1 and 90 seconds (Instagram Reels max). Operates on a parent job — possessing the parent `source_job_id` is the capability, no upload step. Two-call flow: (1) call with `source_job_id` + `start` + `end` (in source seconds) to receive {job_id, payment_challenge}; (2) pay via MPP and call with `job_id` + `payment_credential` to start processing. Poll get_job_status(job_id) for completion; output is role `clip-vertical-video` (the `.mp4`). Flat price: $0.50 per clip. Payment: pay by credit card via the Stripe Checkout link (open the returned `payment_url` in any browser) or Tempo USDC via mppx. Optional `profile` parameter selects the encoding profile (default `tiktok-primary`). Allowed values: `tiktok-primary` (1080×1920, fast preset, CRF 22), `tiktok-primary-720p` (720×1280, CBR 3 Mbps — half-resolution mobile-optimized, ~40% faster wall time), `instagram-reels` (1080×1920, slow preset, CBR 4 Mbps), `instagram-stories` (same encode shape as instagram-reels). All four profiles loudness-normalize identically. Optional `subject` parameter controls reframing (default `center`, preserves today's behavior): `auto` locks onto the longest-tracked face from the parent's subjects-sidecar (or runs inline detection if the parent has none); `subject_id` (with `subject_id` param naming a face_N from the sidecar) locks onto a specific subject; `follow` switches crop between active speakers across the clip using the sidecar's active_speaker_timeline; `manual` accepts caller-supplied framing via `subject_box: {x, y, w, h}` (source pixels) or `subject_x_offset` (direct crop x). Sidecar shape at /.well-known/weftly-subjects-v1.schema.json. auto/subject_id/follow fall back to center if detection or sidecar resolution fails — the paid job always delivers a clip. Source must be a horizontal video (wider than 9:16) — already-vertical or square sources are rejected. Source must still be in storage (72h TTL for find_clips parents, 24h elsewhere — check `expires_at` from get_job_status on the parent). Pair with `find_clips` ($2.00/video) to pick a moment first, then call this to get a download-ready vertical mp4 in under 5 minutes. Multiple extract_vertical_clip calls against one parent are independent paid jobs. Failed jobs auto-refund.
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  • Routes a prompt to the best available LLM. Two backends: 1. DEFAULT — Hugging Face (Qwen2.5-7B, free with API key) 2. PREMIUM — OpenVecta (GLM-5.2 and more, set provider:'openvecta') Use ONLY when you need external LLM help. Never for things you can answer from context. Returns: { response: string, model: string, provider: string, tokens_used?: number }
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  • Current & trending AI MODELS from the open-model ecosystem (Hugging Face) — name, org, task, popularity (likes/downloads) and release date. Use for "what AI models are trending / newest / what's the latest <X> model". This is the OPEN side (Llama, Qwen, DeepSeek, Mistral, Gemma, Phi…); for the closed flagships (GPT, Claude, Gemini, Grok) with pricing & versions use search_ai_models. Args: query: search a model name (e.g. llama, qwen, whisper). org: filter by org/author (e.g. meta-llama, deepseek-ai, Qwen, mistralai, google). task: text-generation (default), text-to-image, automatic-speech-recognition, … or 'any'. sort: trending (default) | newest | downloads. limit: max results. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.
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  • Simulates a hypothetical Tesouro Direto bond operation at a given trade date and rate, returning the full set of metrics for that operation: price (PU), quotation, Macaulay/modified duration, and optionally the cash-flow schedule (coupons or installments). USE THIS for ANY question about a Tesouro Direto bond — price, quotation, duration, sale proceeds, payment schedule, etc. Single tool, all metrics. Bulk: up to 10 items per request (5 if any item sets includeSchedule). Output is discriminated by `productFamily` ('prefixado' | 'ipca' | 'renda-educa'). The `schedule` field defaults to `null` to keep responses compact (a Renda+ schedule alone has 240 entries). It comes populated as `[{ date, businessDays, flow, cashFlowBrl, presentValue, kind, number? }]` (kind: 'coupon' | 'principal' | 'installment') — `flow` is the nominal cash flow before discounting (STN-style flow, not a currency amount); semantics depend on `kind`. `cashFlowBrl` is BRL per one bond unit (face 1000): `flow/100 ×` settlement snapshot of 1000 / `vnaProjected` / `vniProjected`, truncated to centavos (T-2 floor) — not a forecast of BRL actually credited on each payment date for indexed families (VNA on IPCA+ with coupons, VNI on Renda+/Educa+; both move with IPCA accrual); discounted sum of `cashFlowBrl` may differ slightly from PU (T-6). Only when the caller passes `includeSchedule: true` AND the bond family supports a schedule (zero-coupon families like prefixado / ipca-mais never have one). Whenever a field is null, a structured warning in `meta.warnings[]` with `code` and `field` explains why. Every response also carries a `calculation` object (discriminated by `kind`: 'prefixado-zero' | 'prefixado-semestrais' | 'ipca' | 'renda-educa') with the intermediate values used by the engine — business days to maturity, truncated discount exponent, discount factor, VNA/VNI base + projected, IPCA monthly rate, and (when auto-fetched) `automaticIpcaValidFrom`. For IPCA-linked families the `indexProjection` also splits the two projection legs: `monthsBeyondVnaBase` / `monthsCompounded` / `compoundedFactor` (the whole-month forward compounding) versus `exponent` (the sub-month pro-rata). Use it to audit a price against the official methodology without re-running it. Inputs: `tradeDate` (YYYY-MM-DD) is the negotiation date — NOT the settlement date. `side` ('investorBuy' | 'investorSell', optional but recommended when direction is known): semantic intent of the operation. When provided, the engine derives the correct `settlementConvention` automatically — 'investorBuy' -> 'D+1'; 'investorSell' -> 'D+0' if tradeDate >= 2021-09-13 (same-day sell-back rule), else 'D+1'. Omit `side` when simulating for pure pricing purposes without a specific trade direction. `settlementConvention` ('D+0' | 'D+1', optional): advanced override — when omitted, the engine derives from `side` (or defaults to 'D+1' when both are omitted). If both `side` and `settlementConvention` are provided and conflict, the explicit `settlementConvention` is used and a NONSTANDARD_SETTLEMENT_FOR_SIDE warning is emitted. The response echoes `side` (when provided), `settlementConvention` (resolved), and `settlementDate`. `includeSchedule` (default false) opts in to coupon/installment detail. `annualRate` MUST be a decimal string (e.g. '0.1423' for 14.23%, never '14.23'). Bond identification: prefixado/IPCA+ require `maturityDate`; Renda+/Educa+ require `conversionYear` OR `maturityYear`. Optional overrides: `vnaBase`+`ipcaMonthlyRate` for IPCA+ types, `vniBase`+`ipcaMonthlyRate` for Renda+/Educa+ (auto-fetched if omitted). `ipcaMonthlyRate` is the current-month ANBIMA projection used ONLY for the sub-month pro-rata — it does NOT compound whole months forward. For a future-dated valuation (settlement months/years ahead), pass `ipcaForwardMonthlyRate` (assumed monthly IPCA) to project the VNA/VNI base forward over the whole-month gap; it is ignored when `vnaBase`/`vniBase` is overridden. Note `ipcaForwardMonthlyRate` and `annualRate` are orthogonal assumptions: the forward rate is the inflation that grows the VNA/VNI base, while `annualRate` is the REAL yield (the '+X%' of IPCA+X%) that discounts it — for IPCA-linked types it does NOT include inflation. A future-dated valuation needs BOTH set to the future scenario; passing `ipcaForwardMonthlyRate` with today's `annualRate` models future inflation at today's real yield. Warning catalog (`meta.warnings[]`, each `{ code, field?, message }`): `NONSTANDARD_SETTLEMENT_FOR_SIDE` (both `side` and `settlementConvention` were provided and the explicit convention differs from the standard for that side — explicit value was used); `SYNTHESIZED_BOND` (bond not in catalog, synthesized for historical simulation); `SETTLEMENT_BEFORE_LAUNCH` (settlement predates the bond's launch date); `IPCA_STALE` (auto IPCA projection is over 35 days old vs settlement); `IPCA_PROJECTION_MISSING` (historical settlement before the earliest bundled IPCA projection — engine falls back to ipcaMonthlyRate=0, using the VNA of the nearest past day-15 without pro-rata; pass an explicit ipcaMonthlyRate for better precision); `IPCA_FORWARD_RATE_IGNORED` (both a `vnaBase`/`vniBase` override and `ipcaForwardMonthlyRate` were supplied — the override is authoritative so the forward rate was ignored); `IPCA_FORWARD_GAP_NOT_COMPOUNDED` (settlement is N whole months beyond the last published VNA/VNI and no `ipcaForwardMonthlyRate` was supplied — the gap was NOT compounded onto the base, only the sub-month pro-rata; pass `ipcaForwardMonthlyRate` for a future-dated valuation, or refresh bundled VNA data if the settlement is actually near-term); `SUSPICIOUS_RATE` (annualRate > 1.0 — likely passed as percentage); `CALENDAR_LAW_SWITCH` (tradeDate predates Lei 14.759/2023; legacy calendar without Nov 20 holiday is in use); `TRADE_DATE_NON_BUSINESS_DAY` (tradeDate is a weekend, holiday, or market interruption — settlement was rolled forward to the next business day, so D+0 and D+1 may resolve to the same date); `NOT_INCLUDED` (field could exist but caller did not request it — set the matching include* flag, e.g. schedule on coupon/stream families); `CASHFLOW_BRL_SETTLEMENT_VNA_SNAPSHOT` (only when `includeSchedule: true` for `bondType` `ipca-mais-com-juros-semestrais` — each `schedule[].cashFlowBrl` uses the settlement VNA snapshot; see `message`); `CASHFLOW_BRL_SETTLEMENT_VNI_SNAPSHOT` (only when `includeSchedule: true` for `bondType` `renda-mais` or `educa-mais` — each `schedule[].cashFlowBrl` uses the settlement VNI snapshot; see `message`). All values are pre-tax (gross), based on hypothetical inputs — this is a simulation, NOT a real-time market quote nor an offer to trade. IR and IOF apply on actual operations. Each bulk row returns `{ input, ok: true, result }` or `{ input, ok: false, error }`. Before calling, use `catalog_list` if you need to discover available bonds or disambiguate the user's reference.
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  • Shows all details of a single workout: heart rate, pace, cadence, power, intensity zones, elevation, calories, and more. Requires workout_id from get_workout_list. Also shows which sample data (HR time series, speed, GPS etc.) is available — these can be retrieved with get_workout_samples. Parameters: - workout_id: UUID of the workout from get_workout_list
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  • Get the top 5 signals from today's brief as structured JSON — a cheap sample of the full daily_brief. Returns the day's highest-priority items (no prose) so an agent can decide whether to buy the full brief. PAID: $0.50 (vs the full daily_brief price). Defaults to today (UTC). On a 402, pay the returned payment challenge and re-call with the SAME args plus payment_tx=<signature>. An Authorization: Bearer fnet_ key bypasses payment.
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  • Restore and enhance faces in an image using GFPGAN. Detects all faces via RetinaFace, restores quality (fixes blur, noise, compression artifacts), and pastes them back. Optionally enhances the background using Real-ESRGAN. GPU-accelerated, sub-3s latency. Args: image_base64: Base64-encoded image data containing faces (PNG, JPEG, WebP). upscale: Output upscale factor -- 1 to 4 (default: 2). enhance_background: Whether to enhance background with Real-ESRGAN (default: true). Returns: dict with keys: - image (str): Base64-encoded restored image - format (str): Output image format - width (int): Output width - height (int): Output height - upscale (int): Scale factor applied - processing_time_ms (float): Processing time in milliseconds
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  • Résout un numéro de loi/ordonnance/décret vers son identifiant LEGITEXT ou JORFTEXT Légifrance. Utile pour les textes non codifiés (lois, ordonnances, décrets) qui ne sont pas dans la whitelist des 26 codes courts (CC, CP, LIL, LO58, etc.). Une fois le LEGITEXT/JORFTEXT résolu, on peut l'utiliser avec `get_law_article(code=<LEGITEXT>, num=<N>)` pour récupérer un article spécifique. Exemples : - `resolve_law_number("68-1250")` → loi prescription quadriennale des créances publiques (JORFTEXT000000878035) - `resolve_law_number("79-587")` → loi motivation des actes admin - `resolve_law_number("2000-321")` → loi droits citoyens face à l'admin Args: numero: format "YY-NNNN" ou "YYYY-NNNN" (ex: "68-1250", "2000-321") Returns: `{numero, legitext, titre_section, date_debut, articles_count, source_url}` ou `{error}` si introuvable.
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  • Lock in a full blind specification: product, dimensions, colour, and mount type (inside recess or outside face-fix). Validates that the colour exists and is in stock, then prices the blind. Returns a configuration summary to pass directly into create_cart. Call check_colour_stock first if availability is uncertain.
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  • Materialize a memory or freeform note as a post-item in the space. Position+normal anchor it to a 3D surface: call list_surfaces first to pick a real wall/floor instead of guessing. Put it at EYE LEVEL (a wall face around world y 3.5 to 4), not the foundation row (world y 2.0) where it reads as a floor sticker. Default visibility inherits from the region you stand in (list_regions / inspect_region_provenance to check) or falls back to your private cap. Pass memoryId to link this post to a reflection row in agents.memories. Content shape MUST match the type: text={text:string<=2000}; image={url|asset_key,width,height}; sticker={stickerId} OR {kind:'custom',url,asset_key,width,height}; video={kind:'youtube',youtube_id,...} OR {kind:'streamVideo',stream_uid|hls_url|asset_key|url,...}; card={title?,body?,sourceUrl?,layout?,composition?,...}. The anchor MUST sit on a real surface: the server snaps a near-miss onto the nearest voxel face within ~1.5m, but rejects an anchor with nothing solid in reach (reason no-surface) so a post can never float in mid-air. Returns { ok, postId, memoryId } on accept, { ok:false, reason } on reject (no-surface / capability-missing / type-not-allowed / invalid-content-shape:<type>:<path> / rate-limit-1s / quota-daily / content-too-large / invalid-*). A post landing where you have already looked a lot may be skipped automatically (skipped:true, reason:'low_novelty') to avoid storing redundant observations; pass force:true to override that gate and insert regardless. Delete your own posts with delete_memory_post.
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  • Use to access the Hugging Face Hub. Navigate resources with ls, cat, find, stat, and search over hf:// URIs. Roots: hf://models, hf://datasets, hf://spaces, hf://buckets, hf://collections, hf://papers, hf://docs. For papers, ls hf://papers/ARXIV_ID to discover related resources; cat hf://papers/ARXIV_ID/paper.md or metadata.json. Documentation paths include the current version from each product's llms.txt manifest. Grammar; each token below is one args array element: ls URI [(-R|-r|-lR|-laR|--recursive)] [(-l|-a|-la|-al|--long)] [--glob GLOB] [(-type|--type|--entry-type) TYPE] [--sort SORT] [(-limit|--limit) N] cat URI [RELATIVE_PATH] [(-offset|--offset) N] [(-max-bytes|--max-bytes) N] stat URI [RELATIVE_PATH] find URI [(-R|-r|--recursive)] [(-name|--name|--glob) GLOB] [(-path|--path) GLOB] [(-type|--type|--entry-type) TYPE] [(-limit|--limit) N] search URI [QUERY...] [(-type|--type|--entry-type) TYPE] [--sort SORT] [--tag TAG] [--kind mcp] [(-limit|--limit) N] TYPE = file|dir|repo|bucket|collection|paper|link. Type aliases: f=file, d=dir, l=link, model|dataset|space=repo. SORT = createdAt|downloads|likes|lastModified|likes30d|trendingScore|mainSize|id|trending|upvotes. URI starts with hf://. QUERY and GLOB are each one string token. Search URI: hf://models|datasets|spaces[/OWNER], hf://collections[/OWNER], any hf://docs scope, or exactly hf://papers; not hf://. Repository and collection searches may omit QUERY to browse or filter; documentation and paper searches require it. Search joins multiple positional QUERY tokens with spaces. Cat and stat join one RELATIVE_PATH token to URI. Long-list flags are accepted for compatibility; hf_fs listings are already structured, so they do not alter output. Find is already recursive, so recursive flags are accepted without altering behavior. Space search: hf://spaces uses semantic search; repeat --tag to require tags, or use --kind mcp for --tag mcp-server. hf://spaces/OWNER uses owner-scoped keyword search. Documentation: ls hf://docs for products; search any docs scope; use returned hf:// URIs verbatim. Trending listings: ls hf://models/trending, hf://datasets/trending, or hf://spaces/trending. They return up to 20 entries. Trending paths imply trending order; --sort trending|trendingScore is redundant but valid. Trending papers: ls hf://papers/trending. TYPE filters mixed results; omit it when the URI already fixes the result type. Limits and path-specific behavior are documented at hf://README.md. Omit --limit and --sort unless the request asks for a cap, ordering, or exhaustive results. No pipes, redirects, shell expansion, or multiple commands.
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  • **One-call bootstrap for 'video-call me' — talk to the human out loud, face to face.** Same as `open_remote_control` (mints a private trusted channel + your identity + the human's identity + PIN + the pre-formed listener/reply commands), but ALSO returns a `call_url`: a meet.rogerthat.chat/call link that opens a Google-Meet-style video-call UI where your replies are spoken aloud and the human talks back by voice. Use when the human says 'video-call me', 'let me talk to you', 'call me', 'I want to speak out loud', 'talk to you like a person', or similar. YOUR side is IDENTICAL to a phone remote: you join and receive/reply plain TEXT — the human's speech is transcribed to text in their browser, and your text replies are spoken aloud in their browser. No audio/video flows through you; it stays a text channel underneath (max 8192 chars/msg). After this call: (1) `join` with the returned channel_id + token + agent.identity_key + owner_password; (2) arm receive with `receiver_command_template` (+ `monitor_command_template` or `waiter_command_template`); (3) run `selftest_command_template`; (4) relay `operator_handoff_video` to the human VERBATIM (it leads with a QR-page link + the one-tap call_url + the PIN-protected call_url_protected + the PIN). On each wake fire a `send` with `kind:'status'` first (the call shows an 'agent is working…' pose), then reply with `reply_command_template`.
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  • Returns Rudraksha bead recommendations per planet. SECTION: WHAT THIS TOOL COVERS For each of the nine planets, returns: mukhi (face count), presiding deity, exact beej mantra, recommended metal for stringing, wearing day, mala bead count (108), recommended wearing finger, and spiritual benefits. SECTION: WORKFLOW BEFORE: asterwise_get_natal_chart — identify planets needing support. AFTER: None. SECTION: INPUT CONTRACT planet (optional): One of Sun, Moon, Mars, Mercury, Jupiter, Venus, Saturn, Rahu, Ketu. Omit to get all nine planets. SECTION: OUTPUT CONTRACT Single planet: data.planet, data.mukhi (int), data.presiding_deity, data.mantra, data.metal, data.wearing_day, data.mala_beads (int), data.wearing_finger, data.benefits All planets: data.planets{} — object keyed by planet name SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (upstream): Unknown planet name → MCP INTERNAL_ERROR INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_gemstone_recommendations — Ratna-style gemstones from natal chart. asterwise_get_puja_suggestions — ritual worship, not bead recommendations.
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  • Paid tier only. Calling this without an authenticated CivilQuants account returns TIER_INSUFFICIENT — sign up at https://civilquants.com/pricing or use the free-tier alternative compute_cantilever_wall. Reinforced soil retaining wall (RSW) using granular fill reinforced with horizontal layers of geogrid or steel tie strips, with one of four facing systems: precast concrete panels (Reinforced Earth™ / VSL Retained Earth style), segmental modular blocks (Allan Block / Keystone), wraparound soft face (vegetated or biodegradable mat), or Terramesh-style steel mesh face (re-using the S29 gabion vocabulary). The SECOND member of the earth_structures L1 leaf (after gabion_wall at S29), the SIXTH member of the wall family, the FIFTH structural system, and the FIRST wall family member with a face-area-primary measurement basis (m² of face + m² × layers of geogrid, both m²; every prior wall body was m³). 17th use of the classed-then-legacy attribute discrimination pattern. Routes via four new WorkCategory entries (RSW_FACING_PANEL, RSW_MODULAR_BLOCK, RSW_WRAPAROUND_FACE, RSW_TIE_STRIP) plus the GEOGRID category (whose handler is gap-filled in CESMM4/NRM2/MMHW this session) plus re-use of GABION_BASKET on the STEEL_MESH_FACE variant. Codes: CESMM4 E.8.4-8 (Class E Earthworks §E.8 stabilisation), NRM2 5.23-27 (Group 5 Excavating and filling), MMHW 600.15-19 (Series 600 Earthworks; SHW Cl. 624 reinforced earth retaining structures), and SMM7 D41.3/D41.4/D41.5/D20.21/D20.14.2 (D41 — Crib walls / gabions / REINFORCED EARTH — the NAMED home, now serving TWO wall families). Eight variant presets exercise all four RSWFacing values (2/2/2/2 split). Example params: wall_height=6 m (0.5–12), wall_length=40 m (5–200), depth_into_fill_m=4.5 m (0.35–15). Example call: {"params": {"wall_height": 6, "wall_length": 40, "depth_into_fill_m": 4.5}, "standard": "MMHW"}. Omitted parameters use sensible engineering defaults. Pass deliverables=["xlsx","dxf","pdf"] (any subset) to also receive one-shot download URLs in the same call: Excel BoQ (both tiers, watermarked free) plus the dimensioned DXF (CAD) and PDF drawing sheets (paid tier).
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