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443,740 tools. Updated 2026-08-11 14:13

"Spring Boot" matching MCP tools:

  • REAL action: book at a network venue on the user's behalf. provider_id + item (the venue interprets it). Doctor: item={date,time}. Hostel: item={check_in,check_out,guests,promo_code?} — do NOT ask for name/contact: identity comes from a one-time Telegram consent. Spa: item={date,time,procedure}. Hostel: book returns a HOLD with payment_url; the booking is finalized by PAYMENT, not by this call. CONSENT: do NOT ask for approval up front — just call book. ONLY if the call returns consent_url/telegram.consent_url (linked=false = not yet authorized at this venue) do you show that link to the user for a one-tap approval in Telegram, then retry. If consent_url_native (tg://...) is present, try to open it on the user's device (open/xdg-open/Start-Process — launches Telegram directly if installed). BUT tg:// fails SILENTLY without Telegram — so ALWAYS also show the user consent_url (https): t.me opens the app or offers to install it. Open once, then retry. If already authorized (consent given earlier) book succeeds IMMEDIATELY, with no prompts. Never ask for consent when you already have it.
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  • Blend up to 12 colors into one. Each color may be a hex (#d2bc93), a CSS name (red), an RNV brand name (brand gold, near-black), or a saved-palette reference (Spring line, or 'Spring line:2' for its 2nd swatch). Optional integer weights bias the blend (defaults to equal). mode selects the model: rgb/hsv/lab are digital blends (lab is perceptual and the default, best for on-screen color); paint mixes pigments via Kubelka-Munk physics (colors darken like real paint, use it for physical-media matching); ryb is the artist's color wheel; cmy is subtractive like printer inks. Returns hex and rgb. Read-only and deterministic: it computes a result and stores nothing, so it is safe to call repeatedly with no side effects. Use to combine multiple colors into a single blend; to convert one color between formats use convert_color, and to measure how far apart two colors are use color_difference.
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  • Generate a color harmony from a base color. base accepts a hex, CSS name, RNV brand name, or saved-palette reference (e.g. 'Spring line:2'). scheme is one of: complementary, analogous, triadic, split-complementary, tetradic (a.k.a. square), monochromatic, compound. Returns a list of hex colors. Read-only and deterministic: it derives the colors from the base and stores nothing, so it has no side effects and is safe to call repeatedly. Use to expand one base color into a related set; to blend existing colors into a single color use mix_colors, and to persist a set you like use save_palette.
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  • Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).
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  • Render a NATIVE-STYLE TEMPLATE ad from pure HTML — no AI video/image model in the loop, renders in ~30 seconds for a couple of credits. Perfect for native-feel social ads at volume. YOU author the content (short, casual, believable — never marketing-speak). Templates (pass as config.template): 'imessage-chat' (VIDEO ~15s: a real-looking iMessage thread where a friend reveals the product as a rich-link card; config: { thread: { contactName, messages: [{from:'them'|'me', text?, product?:{image,title,domain}}] }, theme?:'dark'|'light', endCard:{headline,cta,domain,logo?,color} } — 4-6 short lowercase bubbles, product card mid-thread from 'me', 1-2 excited replies after); 'chatgpt-chat' (VIDEO: a ChatGPT answer streams the punchline; config: { question, answer (may **bold** the brand), productImage?, endCard }); 'apple-notes' (VIDEO: an iPhone note types itself out; config: { title, lines: string[], theme?, endCard }); 'value-prop' (VIDEO ~17s kinetic typography: config: { hook (≤40 chars), claims: string[] (3-5 COMPLETE phrases, ≤6 words / ≤34 chars each — a finished thought, NEVER a clipped clause like 'Looks good on any'), productImages: string[] (2-3 DISTINCT photos — one rotates per card), palette: string[], endCard }); 'static-mockup' (IMAGE: config: { style:'imessage'|'notes'|'card', size?:{w,h}, ...style fields }); 'airdrop-carousel' (VIDEO ~10s: an iOS AirDrop share card springs up and cycles 3-16 REAL product photos to a full-lineup payoff; config: { brandName, products: [{image, title?}], contactLine?, endCard }); 'app-ui-tour' (VIDEO ~12-16s for APP brands: floating-iPhone mockup walks through REAL app screenshots with kinetic captions; config: { hook?, appName, iconImage?, beats: [{screenImage, caption}] (2-6), palette?, fontStack?, endCard }); 'imessage-cascade' (VIDEO ~12s: iOS notification banners spring in and stack over a blurred backdrop; config: { notifications: [{sender, text}] (4-8), backgroundImage?, endCard }); 'photo-grid' (VIDEO ~8s: collage assembles real photos one at a time; config: { title?, photos: [{image, label?}] (4-9), palette?, fontStack?, endCard }); 'vignette' (VIDEO ~12s: cinematic Ken-Burns hero film; config: { hook, lines: [2-4 ≤40ch], heroImage, palette?, fontStack?, endCard }); 'kinetic-type' (VIDEO ~9-15s typographic motion design with NO VOICEOVER — it is NOT a silent asset: it always carries its own synthesised SFX (whoosh/tick/chime) and, once a curated track is on file, the family's loudest music bed at -16 LUFS; config.music:'off' silences the bed but never the SFX: 3-6 short phrases each land word by word on a full-bleed brand card (product beats caption the phrase over the photo instead), the longest word picked out in the brand accent, and a skewed accent slab wipes every cut; supply productImages and every OTHER beat becomes a full-bleed product shot with its phrase captioned over it — with none it renders as pure typography, so it needs NO photos; config: { phrases: string[] (3-6, ≤34 chars each — punchy, declarative, ONE idea per phrase, a finished thought never a clipped clause), productImages?: string[] (up to 4 DISTINCT photos), palette?: string[], fontStack?, endCard }); 'myth-vs-fact' (VIDEO ~15-26s VO-FIRST kinetic explainer with a real VOICEOVER — the family's ONE paid-audio format: a calm-authority read busts 2-4 myths, each MYTH line slamming in with a red per-line strike then the counter FACT line landing bold+affirmative, word-level KARAOKE lighting each word as the VO speaks it; config: { pairs: [{ myth (≤50ch, the common wrong belief), fact (≤60ch, the corrective truth — wrap its payoff phrase in [brackets] to accent it) }] (2-4), palette?, fontStack?, endCard }. Real product truths only — NEVER invent stats. Costs the flat template credits PLUS a small voiceover charge); 'carousel' (MULTI-IMAGE: 5-10 branded 1080×1080 PNG slides for Meta/LinkedIn/IG carousels — returns an images[] array, one PNG per slide; config: { cover: { hook?, title }, slides: [{ headline (≤8 words), support? (≤16 words), stat?: { value, label } }] (3-8; a stat slide is a REAL user-supplied number like '94%' or '40k+' + a label, never invented), cta: { headline, cta?, domain? }, productImage?, logo?, palette?, fontStack?, endCardColor? }). Every VIDEO format except myth-vs-fact (VO-first, deliberately dry) also gets a mood-matched MUSIC BED when a curated track is on file (the library ships empty — no track means no bed, never a paid generation) under its own SFX, from the curated library — free, no model, no extra credits; set config.music:'off' for a silent cut or a mood name (upbeat/calm/warm/epic/tense/playful/elegant/hype/chill/dramatic) to re-mood it. Image URLs may be any public URL — the server localizes them. Spends a couple of credits.
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  • Retrieves the interactions between the query proteins. Use this method only when you specifically need to list the interactions between all proteins in your query set. If user asks for 'physical' or 'complex' use 'physical' network type. - For a **single protein**, the network includes that protein and its top 10 most likely interaction partners, plus all interactions among those partners. - For **multiple proteins**, the network includes all direct interactions between them. - If the user refers to "physical interactions", "complexes", or "binding", set the network type to "physical". - STRING does not store or report information about self-interactions/homomers; if asked, explain the limitation. If few or no interactions are returned, consider reducing the `required_score`. For large query sets (>50 proteins), consider increasing the `required_score` (e.g. ≥700) to focus on high-confidence interactions and avoid overly dense networks. - Expand the names of score sources: `nscore` (neighborhood), `fscore` (fusion), `pscore` (phylogenetic profile), `ascore` (coexpression), `escore` (experimental), `dscore` (database), `tscore` (text-mining)
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Matching MCP Servers

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  • Federated commerce search across independent WooCommerce merchants. Keyless, read-only MCP server.

  • Search books and authors across Open Library, the Internet Archive open catalog.

  • "What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.
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  • Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.
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  • Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
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  • Query verified ERCOT wholesale electricity prices — ERCOT's Day-Ahead Market Settlement Point Prices ($/MWh, EMIL NP4-190-CD), the price cleared the day before each operating day at every ERCOT (Texas) settlement point, served hourly. Returns cited prices for each (`settlement_point`, `delivery_date`, `hour_ending`): the per-hour `price_usd_per_mwh` in detail records, plus `avg_price_usd_per_mwh`, `min_price_usd_per_mwh`, and `max_price_usd_per_mwh` over the result scope. Each `settlement_point` is one of ERCOT's locations — a trading Hub (e.g. `HB_NORTH`, `HB_HOUSTON` — the regional benchmark prices), a Load Zone (e.g. `LZ_HOUSTON`), a Resource Node (one generator's connection point), or a DC-tie — and `settlement_point_type` carries ERCOT's OWN verbatim classification code (`HU`/`LZ`/`RN`/`LZ_DC` and finer codes) so an agent can tell a regional benchmark from a single-plant node. Filter or group by `settlement_point`, `settlement_point_type`, `hour_ending`, or `delivery_date`; filter a date window with `delivery_date_from` / `delivery_date_to`, or one day with `delivery_date`. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"settlement_point": "HB_NORTH", "delivery_date": "2026-06-20", "group_by": ["hour_ending"]}` for the North hub's 24 hourly day-ahead prices; `{"delivery_date": "2026-06-20", "group_by": ["settlement_point_type"]}` for the average price by location type. With no date filter the result defaults to the latest delivery day with prices (it does not scan all history); served delivery coverage begins 2014-05-02 from ERCOT's official Data Access Portal archive. Historical settlement-point names remain exactly as published, and names absent from the current pinned type list have a null type rather than being rewritten. Returns JSON with citations and optional row-level records when `include_records` is true; every value carries `source`, `as_of` (the delivery day), and a `source_row` verifiable with get_source_evidence_v1. A price is INTENSIVE ($/MWh): it is AVERAGED, min'd, and max'd over a scope — NEVER summed (a "total price" is meaningless, so no sum is offered). An average across more than one settlement point (e.g. a hub and a resource node together) is indicative, not a settlement value — group_by `settlement_point` for the per-point series, or filter to one point/type. This is the DAY-AHEAD hourly market, NOT real-time / 5-minute prices. ERCOT's day-ahead settlement-point price is a TOTAL only — there is no energy/congestion/loss component split and no loss component, and none is synthesized. ERCOT's own hour-ending label (`01:00`..`24:00`) and `dst_flag` are carried verbatim (a day is 24 hours normally, 25 on the fall-back DST date with `02:00` repeated, 23 on spring-forward). A settlement point is an electrical/aggregate location, not a plant — ERCOT supplies no county or lat/lon, so the only geography anchor is `state` = TX. This is a PRICE ($/MWh) — not capacity (MW) or generation (MWh): for installed/operating capacity use query_power_capacity_v1, for electricity generated use query_power_generation_v1. ERCOT only — prices are NEVER blended, averaged, or compared across ISOs (each ISO's market design and redistribution license differ); this tool serves ERCOT's day-ahead settlement-point prices alone.
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  • Blend up to 12 colors into one. Each color may be a hex (#d2bc93), a CSS name (red), an RNV brand name (brand gold, near-black), or a saved-palette reference (Spring line, or 'Spring line:2' for its 2nd swatch). Optional integer weights bias the blend (defaults to equal). mode selects the model: rgb/hsv/lab are digital blends (lab is perceptual and the default, best for on-screen color); paint mixes pigments via Kubelka-Munk physics (colors darken like real paint, use it for physical-media matching); ryb is the artist's color wheel; cmy is subtractive like printer inks. Returns hex and rgb. Read-only and deterministic: it computes a result and stores nothing, so it is safe to call repeatedly with no side effects. Use to combine multiple colors into a single blend; to convert one color between formats use convert_color, and to measure how far apart two colors are use color_difference.
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  • Generate a color harmony from a base color. base accepts a hex, CSS name, RNV brand name, or saved-palette reference (e.g. 'Spring line:2'). scheme is one of: complementary, analogous, triadic, split-complementary, tetradic (a.k.a. square), monochromatic, compound. Returns a list of hex colors. Read-only and deterministic: it derives the colors from the base and stores nothing, so it has no side effects and is safe to call repeatedly. Use to expand one base color into a related set; to blend existing colors into a single color use mix_colors, and to persist a set you like use save_palette.
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  • Compute the exact Eveoy price for a pilot. Pricing mirrors eveoy.com/order. Base: customers_per_location × locations × $24.99. Optional guaranteed purchase (guarantee_type "visit_purchase"): every shopper also buys your chosen SKU at your register — add the SKU price (in cents, tax included, $5–$100) at cost, no item fee; the item money rings back into your till. Optional shopper bonus ($20–$200 per shopper, 33% platform fee on the bonus only — the only platform fee): every $20 unlocks +1 photo and +1 follow/like/comment set per shopper, each capped at +3. Total = units×2499 + units×sku + round(units×bonus×1.33) cents — the same server-side math Stripe charges. The marketing-default $999 pilot is 40 customers at 1 location, visit-only. Floor 20/location, ceiling 1,000/location, locations cap 50. Use this when the user wants to: - Get a price for a specific shopper/customer count and location count ("price 200 shoppers across 3 stores") - Quote a pilot with a guaranteed purchase and/or a shopper bonus, with the full fee breakdown - Get a budget estimate or compare cost across pilot sizes - Confirm the per-shopper rate before booking Trigger phrases include: "how much does eveoy cost", "price for 500 shoppers", "what's a pilot cost with a guaranteed purchase", "what does the bonus cost", "eveoy pricing", "quote me a pilot for 100 shoppers in 4 stores". Returns: { customers_per_location, locations, total_customers, unit_price_usd, total_usd, formatted_total, ugc_photos, is_starter_tier, guarantee_type, top_sku_price_cents, shopper_bonus_cents, fee_breakdown { base_cents, sku_cents, bonus_cents }, bonus_tiers }. Reflects the exact math the eveoy.com/order backend applies; never returns a number the form would reject. Do NOT use this for: - General "what is eveoy" questions (use ask_eveoy) - Industries served (use list_industries) - Custom volume contracts beyond the 50-location ceiling — route the buyer to support@eveoy.com Cost: free. Latency: <100ms. Read-only. Idempotent. Deterministic.
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  • Return the list of industries Eveoy serves — 23+ B2C sectors across retail, food, beauty, hospitality, pets, and more. Use this when the user wants to: - Check whether Eveoy supports their vertical ("do you do coffee shops?") - See the full list of supported industries - Confirm an industry before pricing or booking a pilot Trigger phrases include: "what industries does eveoy support", "do you work with QSR", "list verticals", "is eveoy good for fitness studios", "what categories", "what sectors". Returns: { industries: string[], count: number, notes: string }. Each entry is a canonical sector name suitable for downstream use. Do NOT use this for: pricing (use get_pricing) or general Eveoy questions (use ask_eveoy). Cost: free. Latency: <100ms. Read-only. Cacheable. Deterministic.
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  • Generate a cohesive SET of custom images on a SOLID-COLOR background, each one a separate isolated subject sharing one background and one visual style (icons, logos, game assets, UI elements, sprite/asset packs). The style parameter says how everything is drawn; the subjects parameter says what to draw. The style can also come from reference images via the styleReferences parameter - alone, or best combined with the style text (text plus references holds a style tightest), so an existing set can be extended in its original style across many calls. Returns a zip download URL. Each call costs 1 credit. Run generation calls sequentially, never in parallel - only one generation runs at a time per API key. For TRANSPARENT-background output, use generate_transparent_image_set instead (3 credits per call). For a SINGLE composed picture or a full-bleed scene (hero image, banner, character portrait, environment), use generate_illustration instead. Output formats: PNG (lossless), JPEG, or WebP. Images can be delivered at a specific size or at their native resolution. Size and quality considerations: Omitting the size parameter delivers images at their native resolution with zero scaling, which produces the highest quality results and is recommended when images will be post-processed, composited, or resized downstream. Native output dimensions vary between generations and track the subject count - roughly 650-950px per side for small sets, down to roughly 400-650 at the full 18; fewer subjects means larger native images. Specifying a size (e.g. 512) guarantees consistent dimensions across all images and generations but applies resampling which may soften fine details. IMPORTANT - style and subject description rules for best results: The style applies to every image in the set, so it is what keeps them visually consistent. Put HOW the images are drawn (technique, palette, surface treatment) in the style, and make each subject description only about WHAT that one subject is, not how it looks. A quick test for any phrase: is it WHAT the subject is, or HOW it is drawn? HOW belongs in the style, shared across the set. - Get the style and the subject descriptions right with the user before you call. When their request puts how an image is drawn, a background, or a scene into a subject description (or names the subjects to draw in the style rather than as separate entries in the subjects list), fix it as you compose the call: routine moves of shared technique into the style you can just make, but when a change drops or alters something they explicitly asked for, tell them what you are adjusting and why first. Each call costs a credit, so it is worth getting this right up front rather than spending one on a framed or scene-filled result. - The style describes the visual treatment of the images (e.g. 'watercolor', 'pixel art', 'stained glass'). It must NOT mention background color, image count, layout, or sizing. - Do not list the subjects to draw in the style (e.g. 'illustrations of a fox, an owl, and a deer'); the style is only the shared visual treatment, and the subjects belong in the subjects list, one per entry. A category or theme word is fine (e.g. 'insect illustration'). - Do not put background color or background descriptions in the style or subject descriptions. - Avoid framing the style as a type of painted canvas ('oil painting', 'acrylic painting', 'gouache painting', 'pastel painting'). These tend to produce each image as a rectangular framed canvas with its own colored background, rather than an isolated subject. Prefer 'illustration' or a specific technique: 'watercolor illustration', 'pen-and-ink sketch', 'ink wash', 'relief-etching', 'pastel drawing', 'woodblock print'. - Avoid color-field or atmospheric phrasings in the style ('luminous backgrounds of violet, rose, and gold', 'set against jewel-tone fields', 'dreamlike rainbow atmosphere'). These instruct the image model to fill each image with colored atmosphere, producing framed compositions rather than isolated subjects. Describe only the linework, palette, and technique of the subjects themselves. - Do not describe an aged, weathered, cracked, or textured surface, ground, wall, panel, or paper that the whole artwork sits on ('on aged wood', 'cracked fresco wall', 'aged parchment surface'); name the art tradition(s) or style(s) instead ('fresco-style illustration'). Texture that belongs to a subject's own material is fine ('a weathered bronze shield', 'a cracked ceramic vase'). - No captions, labels, or annotations. Text that is part of the depicted object is fine (e.g. 'STOP' on a stop sign, 'EXIT' on an exit sign). - No grid lines, borders, frames, or separators. - No overlapping or collage-style arrangements. - Do not connect the subjects to each other or give them shared physical elements: no wires, cords, chains, ropes, ribbons, vines, or threads running between subjects, no frame or banner they share, no phrasing like 'connected by' or 'strung together', and no single continuous line or tube forming multiple subjects. Each subject must be drawable in complete isolation; connections inside one subject (a chain on an amulet, laces on a boot) are fine. - No dramatic/long drop shadows (subtle shadows are fine). - Image descriptions should describe WHAT to depict, not where to position it. - Each image is ONE isolated subject, not a scene. Describe the subject with its pose or action and anything it directly holds, rides, or interacts with, but not the surrounding setting, environment, landscape, or sky. For a single composed scene (a figure set within an environment), use generate_illustration instead. - Do not use size words (large, tiny, small, etc.) on the overall image subject (e.g. 'a large elephant', 'a tiny mouse') - all images are produced at the same size. Size words on details within the image are fine (e.g. 'a plate with a small insignia'). - Maximum 18 images per generation. Do not put the image count in the style. - Subjects must be distinct: entries that differ only in case, punctuation, or spacing count as the same subject and the call is rejected. Explicit filenames must be distinct too (a different extension alone is not distinct). - The style must actually describe a visual style, and each subject must name a drawable subject; text that does not is rejected. - Style description max length: 500 characters. Image description max length: 200 characters each. - Size: each axis between 256 and 512 pixels (e.g. "512" for square, "256x512" for non-square); values outside the range are rejected. Omitting size delivers native resolution, which is also the path to larger images. - If the style check returns a suggested cleanup, show the user the specific changes and get their confirmation, then resubmit the approved prompt with validation set to "skip" so it generates exactly as approved (resubmitting without "skip" re-runs the check and may return further suggestions). See the validation parameter for when to use "skip" and "auto-apply". - If a "Rate limit exceeded" error is returned, wait the suggested number of seconds before retrying. Do not retry immediately.
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  • Generate a cohesive SET of custom images with TRANSPARENT backgrounds, each one a separate isolated subject sharing one visual style (icons, logos, sprites, UI assets that need to drop onto any backdrop). The style parameter says how everything is drawn; the subjects parameter says what to draw. The style can also come from reference images via the styleReferences parameter - alone, or best combined with the style text (text plus references holds a style tightest), so an existing set can be extended in its original style across many calls. Returns a zip download URL. Each call costs 3 credits. If a solid colored background is acceptable for the user's use case, generate_image_set is 1 credit per call instead. Run generation calls sequentially, never in parallel - only one generation runs at a time per API key. Output formats: PNG (lossless) or WebP. JPG is not supported because it has no alpha channel. Size and quality considerations match generate_image_set: omitting size delivers native resolution (best quality, varies between generations); specifying a size resamples to that exact dimension. IMPORTANT - style and subject description rules for best results: The style applies to every image in the set, so it is what keeps them visually consistent. Put HOW the images are drawn (technique, palette, surface treatment) in the style, and make each subject description only about WHAT that one subject is, not how it looks. A quick test for any phrase: is it WHAT the subject is, or HOW it is drawn? HOW belongs in the style, shared across the set. - Get the style and the subject descriptions right with the user before you call. When their request puts how an image is drawn, a background, or a scene into a subject description (or names the subjects to draw in the style rather than as separate entries in the subjects list), fix it as you compose the call: routine moves of shared technique into the style you can just make, but when a change drops or alters something they explicitly asked for, tell them what you are adjusting and why first. Each call costs a credit, so it is worth getting this right up front rather than spending one on a framed or scene-filled result. - The style describes the visual treatment of the images (e.g. 'watercolor', 'pixel art', 'stained glass'). It must NOT mention background color, image count, layout, or sizing. - Do not list the subjects to draw in the style (e.g. 'illustrations of a fox, an owl, and a deer'); the style is only the shared visual treatment, and the subjects belong in the subjects list, one per entry. A category or theme word is fine (e.g. 'insect illustration'). - Do not put background color or background descriptions in the style or subject descriptions. - Avoid framing the style as a type of painted canvas ('oil painting', 'acrylic painting', 'gouache painting', 'pastel painting'). These tend to produce each image as a rectangular framed canvas with its own colored background, rather than an isolated subject. Prefer 'illustration' or a specific technique: 'watercolor illustration', 'pen-and-ink sketch', 'ink wash', 'relief-etching', 'pastel drawing', 'woodblock print'. - Avoid color-field or atmospheric phrasings in the style ('luminous backgrounds of violet, rose, and gold', 'set against jewel-tone fields', 'dreamlike rainbow atmosphere'). These instruct the image model to fill each image with colored atmosphere, producing framed compositions rather than isolated subjects. Describe only the linework, palette, and technique of the subjects themselves. - Do not describe an aged, weathered, cracked, or textured surface, ground, wall, panel, or paper that the whole artwork sits on ('on aged wood', 'cracked fresco wall', 'aged parchment surface'); name the art tradition(s) or style(s) instead ('fresco-style illustration'). Texture that belongs to a subject's own material is fine ('a weathered bronze shield', 'a cracked ceramic vase'). - No captions, labels, or annotations. Text that is part of the depicted object is fine (e.g. 'STOP' on a stop sign, 'EXIT' on an exit sign). - No grid lines, borders, frames, or separators. - No overlapping or collage-style arrangements. - Do not connect the subjects to each other or give them shared physical elements: no wires, cords, chains, ropes, ribbons, vines, or threads running between subjects, no frame or banner they share, no phrasing like 'connected by' or 'strung together', and no single continuous line or tube forming multiple subjects. Each subject must be drawable in complete isolation; connections inside one subject (a chain on an amulet, laces on a boot) are fine. - No dramatic/long drop shadows (subtle shadows are fine). - Image descriptions should describe WHAT to depict, not where to position it. - Each image is ONE isolated subject, not a scene. Describe the subject with its pose or action and anything it directly holds, rides, or interacts with, but not the surrounding setting, environment, landscape, or sky. For a single composed scene (a figure set within an environment), use generate_illustration instead. - Do not use size words (large, tiny, small, etc.) on the overall image subject (e.g. 'a large elephant', 'a tiny mouse') - all images are produced at the same size. Size words on details within the image are fine (e.g. 'a plate with a small insignia'). - Maximum 18 images per generation. Do not put the image count in the style. - Subjects must be distinct: entries that differ only in case, punctuation, or spacing count as the same subject and the call is rejected. Explicit filenames must be distinct too (a different extension alone is not distinct). - The style must actually describe a visual style, and each subject must name a drawable subject; text that does not is rejected. - Style description max length: 500 characters. Image description max length: 200 characters each. - Size: each axis between 256 and 512 pixels (e.g. "512" for square, "256x512" for non-square); values outside the range are rejected. Omitting size delivers native resolution, which is also the path to larger images. - If the style check returns a suggested cleanup, show the user the specific changes and get their confirmation, then resubmit the approved prompt with validation set to "skip" so it generates exactly as approved (resubmitting without "skip" re-runs the check and may return further suggestions). See the validation parameter for when to use "skip" and "auto-apply". - If a "Rate limit exceeded" error is returned, wait the suggested number of seconds before retrying. Do not retry immediately.
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  • Retrieve proteins annotated with a functional term or descriptive text in a single species. You can query for tissues, compartments, diseases, processes, pathways, and domains. IMPORTANT: For cross-species comparisons, run this tool separately for each species. Select relevant model organisms to search or ask user to provide the selection. The results reflect annotation depth within each category; use caution when interpreting. If no results are found, try simplifying the query. For tissue queries, follow BRENDA tissue nomenclature and omit the word "tissue" (e.g. use "skin" instead of "skin tissue"). Output fields: - category: Source database of the matched functional term (e.g. GO, KEGG, Reactome, Pfam, InterPro). - term: Exact identifier for the functional term. - description: The free text description of the term. - proteinCount: Number of proteins annotated with that term - preferredNames: Full protein-name list when `detail_for_term` is set - stringIds: STRING protein identifiers when returned - preferredNames_omitted: True when a row omits the protein-name list - stringIds_omitted: True when STRING identifiers are omitted
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  • ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1419 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,462 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
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  • Find USGS water monitoring sites in a state. Returns a list of monitoring stations with their site numbers, names, and locations. Use site numbers with get_water_levels to retrieve data. Args: state: Two-letter US state abbreviation (e.g. 'CA', 'TX'). site_type: Type of monitoring site. 'ST' for stream/river, 'GW' for groundwater well, 'SP' for spring. limit: Maximum number of sites to return (default 50).
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  • SESSION-RECOVERY · FIRST CALL when a session starts and the user mentions launch / users / growth / customers / metrics / revenue / marketing / what next / shipping. Returns a command-center bootCard with `headline`, `priority`, `cards[]` (each carries kind + label + literal user command + runHandle), and `next` (the one-line prompt). Aggregates: pending approvals + ripe measurements + new engagement + queued prospects + recent launches + manual-publish-pending actions. ChiefLab is stateful and re-summonable — even if the conversation was lost, the IDE was switched, or the runId was forgotten, this call recovers the workspace business state. If the user asked to launch the CURRENT repo, compare boot cards to currentRepoContext/projectName; if the open loop is unrelated, start a fresh launch instead of resuming stale work.
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