"Nike" matching MCP tools:

• get_cashback_rates_by_store_nameA

  GotCashback MCPA

  Finds a store by name and returns its current cashback rates from every cashback portal that lists it — online and in-store, percentage or fixed amount, with 'up to' flags — so the user can compare every portal and see the best. This is the preferred first call for any cashback question that names a store: 'best cashback for Walmart', 'highest Nike cashback', 'cashback at Expedia', 'Walmart cashback today', 'compare Walmart cashback portals', 'Best Buy in-store cashback', 'Dell cashback in Germany'. No prior lookup is needed — do not call get_stores_by_name, get_stores_by_country or get_countries first. Returns every matching store (best match first, one entry per country), each with its 'cashback_rates'; an empty list means no match — retry with a shorter name or without country_code. Use get_cashback_rates_by_store_id only when a store_id is already known, get_gift_cards_by_store_name for gift card discounts, and get_best_deals_by_brand when the user asks where to buy a brand's products rather than about a specific store. Rates reflect GotCashback's current data, refreshed several times a day. Present every returned portal's rate to the user (a table with a link column, not only the best one) and always show each rate's 'url' as a clickable link — cashback is only credited when the user clicks through it.

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• get_cashback_rates_by_store_nameA

  GotCashbackA

  Finds a store by name and returns its current cashback rates from every cashback portal that lists it — online and in-store, percentage or fixed amount, with 'up to' flags — so the user can compare every portal and see the best. This is the preferred first call for any cashback question that names a store: 'best cashback for Walmart', 'highest Nike cashback', 'cashback at Expedia', 'Walmart cashback today', 'compare Walmart cashback portals', 'Best Buy in-store cashback', 'Dell cashback in Germany'. No prior lookup is needed — do not call get_stores_by_name, get_stores_by_country or get_countries first. Returns every matching store (best match first, one entry per country), each with its 'cashback_rates'; an empty list means no match — retry with a shorter name or without country_code. Use get_cashback_rates_by_store_id only when a store_id is already known, get_gift_cards_by_store_name for gift card discounts, and get_best_deals_by_brand when the user asks where to buy a brand's products rather than about a specific store. Rates reflect GotCashback's current data, refreshed several times a day. Present every returned portal's rate to the user (a table with a link column, not only the best one) and always show each rate's 'url' as a clickable link — cashback is only credited when the user clicks through it.

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• polymarket_arbitrageA

  Nice GuidanceA

  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}. FEES: every opportunities[] row and partition_check.arbitrage carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and fee_categories[]. BOTH cost components are modeled: Polymarket's own per-category TAKER FEE (fee = shares × rate × p × (1-p), rates crypto 0.07 / sports-economics-culture-weather-other 0.05 / finance-politics-mentions-tech 0.04, geopolitics and world events fee-free; verified against Polymarket's own docs as of 2026-09-13) and Polygon gas (~$0.02/leg). The taker fee dominates: ~$1.75 per 100 shares on a crypto market at 50c versus $0.02 of gas, so rows that looked profitable before fleet #1927 may now show net_positive:false — that is the correction, not a regression. Each leg is priced at ITS OWN market's rate and price (the fee curve peaks at 50c and falls toward both extremes). fee_basis says where the rate came from: 'payload' (read off the market, the normal case), 'category' (mapped from its fee category), 'fee_free', or 'fallback' (rate unknown — charged at the modal 0.05 rather than assumed free, so an unreadable market is never reported as costless). Where fill_check reprices against live depth, this does NOT double-count that spread cost. 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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• resolution_auditA

  Nice GuidanceA

  Extract the settlement clause of a single Polymarket or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at, the precision of the computation (e.g. "1-minute candle close" vs "60-second trailing average" vs "election outcome"), the evidence standard (official_source | consensus_reporting | any_credible_report | unspecified), and void_handling (cancellation/postponement settlement — reused verbatim from bet_research's cancellation_rule detector, not re-derived). Parses Polymarket's `description` field (fetched via polymarket_market) or Kalshi's `rules_primary` + `rules_secondary` fields (fetched via kalshi_market) with regex + a small vocabulary — no LLM pass, so an unusual clause reports confidence:"low" rather than a guess. Pass `market` as a Polymarket slug/URL or a Kalshi market ticker (e.g. "KXBTCD-26SEP1317-T66999.99"); a Kalshi EVENT ticker (e.g. "KXBTCD-26SEP1317") also works — it picks one representative market under that event, since the settlement mechanism is normally shared across all strikes/legs in one event. Use this before treating a polymarket_kalshi_spread row as a real arbitrage: two ladders that look alike can settle on different sources, at different times, with different precision — this tool is how you check. Pair with resolution_diff to compare two markets directly. KNOWN GAP: idiosyncratic phrasing that doesn't match the vocabulary returns confidence:"low" and evidence_standard:"unspecified" rather than an LLM-guessed answer.

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• preview_productsA

  mcp-serverA

  Get a page of the project's imported products with their real source-attribute values, so you can inspect the data and advise on feed mapping (e.g. see that `brand` holds "Nike" or `color` holds hex codes). Returns {products:[{id, type, attributes:{<sourceCode>:<value>}}], returned, requested, total, offset, hasMore, nextOffset}. attributes keys are source attribute codes (as in list_source_attributes); only catalogued, non-sensitive attributes are included — cost/margin and internal fields are never returned, and empty values are omitted. sample_size is the page window (default 5, max 100); page through the whole catalog with offset — when hasMore is true, re-call with offset=nextOffset until hasMore is false. project_id is OPTIONAL (inferred for a single-project customer). Data must be imported first (see get_import_status). This tool returns real product data values only after you set acknowledge_sensitive:true. The first call (flag absent/false) returns {sensitiveGate:{confirmationRequired:true, itemCount, fields, kind}} with an EMPTY products array — present that gate to the USER, get their approval, then re-call with acknowledge_sensitive:true to receive the actual values. To review the full generated feed output for every product (not just source attributes), use export_feed instead of paging this preview — that is the intended bulk path. Next: pick a template (list_feed_templates) and map attributes (map_feed_attribute).

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• set_store_alertA

  GotCashback MCPA

  Creates or updates an alert that emails the signed-in user when a store's cashback rate or gift card discount reaches the threshold percent or higher. Use it for 'alert me when Nike cashback hits 10%' or 'tell me when Target gift cards are 15% off'. Re-saving an existing alert updates its threshold and re-arms it. At most 10 active alerts per user. Needs the store_id: take it from an earlier result, or find it with get_stores_by_name (optionally with country_code). Requires OAuth sign-in with a GotCashback account and the 'alerts' scope; anonymous callers are prompted to authorize.

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• set_store_alertA

  GotCashbackA

  Creates or updates an alert that emails the signed-in user when a store's cashback rate or gift card discount reaches the threshold percent or higher. Use it for 'alert me when Nike cashback hits 10%' or 'tell me when Target gift cards are 15% off'. Re-saving an existing alert updates its threshold and re-arms it. At most 10 active alerts per user. Needs the store_id: take it from an earlier result, or find it with get_stores_by_name (optionally with country_code). Requires OAuth sign-in with a GotCashback account and the 'alerts' scope; anonymous callers are prompted to authorize.

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• polymarket_fill_riskA

  Nice GuidanceA

  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). FEES ARE NOT MODELLED HERE: vwap_fill_price/profit_usd are GROSS of Polymarket's own taker fee (rate 0.04-0.07 by category — see polymarket_edges/fees.ts), on top of which this tool prices depth-crossing cost; a thin-margin fill that looks clean here can still be net-negative after the fee.

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• kalshi_weather_edgeA

  Nice GuidanceA

  Prices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob (mid), forecast_prob, and edge_pp per strike, plus the settlement clause verbatim. BACKTEST (`backtest_days: N`): scores an archived gridded forecast against the market on settled days and returns brier_market vs brier_forecast with a plain-English `verdict`, so the edge is MEASURED rather than asserted. READ THE WARNINGS — they are not boilerplate. (1) These markets DO NOT settle on the NWS. They settle on The Weather Company (weather.com) at a Kalshi station code such as CLINYC, which the response quotes verbatim; so part of every edge_pp is NWS-vs-Weather-Company disagreement about the same day at the same station, which is not mispricing and not tradeable. `settlement_vs_forecast_basis_f` from backtest mode is that part as a number. (2) The station is DERIVED from the settlement clause, never from the city name: Chicago settles at MIDWAY and New York at CENTRAL PARK, so a city-centre forecast would misprice a whole ladder. A station that cannot be resolved yields rows with no forecast and a reason, never a guessed coordinate. (3) forecast_prob assumes a normal distribution around the NWS high whose width is ASSUMED, not fitted (stated in `distribution_assumption`) — run backtest mode to see whether it is calibrated. (4) edge_pp is gross: no Kalshi fees, no bid-ask. MEASURED RESULT, AND IT IS NOT THE FLATTERING ONE: on the first backtest (KXHIGHNY, 13 settled days to 2026-09-11, 58 market observations) the MARKET beat the forecast — Brier 0.1008 for the market against 0.1594 for the archived gridded forecast, lower being better. So on that sample there is NO forecast edge to sell, and a large edge_pp is more likely to be the model disagreeing with a better-informed market than an opportunity. The measured settlement-vs-forecast basis was 1.7F mean absolute over 8 pinnable days, slightly warm-biased, which is a big share of a typical edge_pp on a 2-degree bracket. Re-run backtest_days before believing any edge; if a later sample reverses this, the numbers say so. NWS is US-only, so the ~30 international Kalshi weather series (London, Paris, Tokyo) return market prices with forecast_unavailable rather than a forecast. Precipitation series are listed but not yet priced. Cities: nyc, chicago, los angeles, miami, austin, houston, denver, philadelphia — or pass `series_ticker` for any other (e.g. "KXHIGHTBOS").

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• release_calendar_marketsA

  Nice GuidanceA

  JOIN of the official release calendar (econ data, the FOMC, FDA decisions, SEC rules) against LIVE Polymarket/Kalshi markets — which scheduled releases land in the next N hours, and which live markets resolve on them. This is a POSITIONING tool, not a speed product: results are cached like every other pack (≤ 60s TTL) and there is no push/webhook — do not use this to try to beat a release, use it to see what is coming and what is already priced. CATEGORIES: econ (CPI, Employment Situation/jobs report, GDP, PCE, PPI, retail sales, housing starts, jobless claims — via fred_release_dates per known release_id, since FRED's own cross-release calendar mostly returns recent actuals, not future dates), fed (the next FOMC meeting's rate decision, via fomc_calendar), fda (PDUFA action dates + FDA advisory-committee meetings, via pdufa_catalysts / fda_adcom_calendar), sec (SEC final rules whose own DATES clause names an effective date in the window, via federal-register recent_rules — usually finds nothing in a short window since SEC rules typically take effect 30–60 days out, which is an accurate answer, not a bug), court (ALWAYS EMPTY today — court-listener has no forward-looking scheduled-hearing calendar, only filing/termination dates, so this category returns zero releases with unsupported:true rather than fabricate one). Omit `categories` or pass "all" for every category. MATCHING AND ITS HONESTY CONTRACT: every release is returned even when it has ZERO matched markets — a release is never dropped just because nothing on Polymarket or Kalshi resolves on it (most FDA/SEC releases will show markets:[]; that is signal, not a gap). Every matched market carries resolves_on_this_release: "true" (the venue's own close/end date sits within ~36h of the release AND the question passed a subject filter — econ and fed only), "likely" (same subject filter, but the venue closes days away from the release date), or "unclear" (a keyword hit with no date to anchor against — always true for the fda category, which has no ladder structure to check a date against). matched_by names the mechanism (a Kalshi series ticker, a Polymarket search query, or an FDA keyword probe) so a caller can judge the match rather than trust a label. scheduled_at carries both `utc` and `et`; econ releases use the standing BLS/Census 8:30am ET convention (FRED's calendar itself has no clock time), FOMC decisions use the 2:00pm ET convention, and FDA/SEC dates are date_only:true (no reliable clock time exists for either). DO NOT treat a matched market as a real arbitrage or a settled fact on its own — a market question sharing tokens with a release name is not proof it settles on that release's own published number. Call resolution_audit / resolution_diff (fleet #1909) on a specific market before sizing anything here. An empty window (zero releases across every requested category) returns error:"no_releases_in_window" with a widen-the-window hint rather than an empty array — econ releases especially cluster on specific dates each month, so a 48h window often straddles a dead stretch.

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• pipeworx_feedbackA

  Nice GuidanceA

  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. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. 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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• search_newsA

  APITube NewsA

  Search news articles using APITube News API with comprehensive filtering. IMPORTANT INSTRUCTIONS FOR QUERY CONSTRUCTION: 1. DO NOT use dots in parameter names directly in the root object. Use nested objects instead. 2. The system will automatically convert nested objects to dot notation for the API. Example: Use { language: { code: "en" } } instead of { "language.code": "en" }. 3. For multiple values in one parameter, use COMMA separation (e.g., "en,ru,fr" for multiple languages). 4. Integer filters (has_*, is_*) accept ONLY 0 or 1 (e.g., has_image=1, is_duplicate=0). 5. Date format: ISO 8601 (YYYY-MM-DD or YYYY-MM-DDTHH:MM:SSZ). 6. Sentiment scores range: -1.0 (negative) to 1.0 (positive). 7. Default sorting: published_at DESC (newest first). 8. Unknown parameters are rejected with an error (-32602) instead of being silently ignored — check the spelling against the list below. 9. By default the response returns id, title, href, published_at, description and source.domain. The article body is NOT included — request it explicitly with fl (e.g. fl: "title,href,body"). AVAILABLE PARAMETERS: ### Content Search - title: Search by article title (supports up to 3 keywords with comma separation) IMPORTANT: a title search covers at most a 31-day published_at window. Omit the dates and the last 31 days are searched; pass a range wider than 31 days and the call fails with 400 ER0110. To cover a longer period, make one call per month-sized window. - ignore: { title: "keyword" } - Exclude articles with specific titles ### Languages (60+ supported) - language: { code: "en,ru,fr" } - Filter by language codes (up to 3) - ignore: { language: { code: "fr" } } - Exclude specific languages ### Categories (IPTC taxonomy) - category: { id: "medtop:04000000" } - Filter by category ID (up to 3) - ignore: { category: { id: "315" } } - Exclude categories ### Topics - topic: { id: "crypto_news,climate_change" } - Filter by topic ID (up to 3) - ignore: { topic: { id: "2" } } - Exclude topics ### Industries - industry: { id: "246771,246772" } - Filter by industry ID (up to 3) - ignore: { industry: { id: "246772" } } - Exclude industries ### Entities - entity: { id: "1278268,1282301" } - Filter by entity ID (up to 3) - ignore: { entity: { id: "315" } } - Exclude entities ### Persons - person: { name: "Elon Musk,Tim Cook" } - Filter by person name (up to 3) - ignore: { person: { name: "John Doe" } } - Exclude persons ### Locations - location: { name: "Tokyo,New York" } - Filter by location (up to 3) - ignore: { location: { name: "Paris" } } - Exclude locations ### Organizations - organization: { name: "Tesla,Apple,Google" } - Filter by organization (up to 3) - ignore: { organization: { name: "Microsoft" } } - Exclude organizations ### Disasters - disaster: { name: "Earthquake,Tsunami" } - Filter by disaster type (up to 3) - ignore: { disaster: { name: "Flood" } } - Exclude disasters ### Diseases - disease: { name: "COVID-19,Influenza" } - Filter by disease (up to 3) - ignore: { disease: { name: "Flu" } } - Exclude diseases ### Events - event: { name: "Olympics,World Cup" } - Filter by event (up to 3) - ignore: { event: { name: "Super Bowl" } } - Exclude events ### Brands - brand: { name: "Nike,Adidas" } - Filter by brand (up to 3) - ignore: { brand: { name: "Puma" } } - Exclude brands ### Authors - author: { id: "123,456" } - Filter by author ID (up to 3) - author: { name: "John Doe,Jane Smith" } - Filter by author name (up to 3) - ignore: { author: { id: "789" } } - Exclude author IDs - ignore: { author: { name: "Bob Jones" } } - Exclude author names - has_author: 1 - Articles with attributed authors (0 for without) ### Sentiment Analysis - sentiment: { overall: { score: { min: 0.5, max: 1.0 } } } - Sentiment score range - sentiment: { overall: { polarity: "positive" | "negative" | "neutral" } } - Sentiment polarity - sentiment: { title: { score: { min: -1.0, max: 1.0 } } } - Title sentiment - sentiment: { body: { score: { min: -1.0, max: 1.0 } } } - Body sentiment - sentiment: { mixed: 1 } - Articles with mixed sentiment (title/body differ) - sentiment: { consistent: 1 } - Articles with consistent sentiment ### Media Content - media: { images: { count: { min: 2, max: 10 } } } - Filter by image count - media: { videos: { count: { min: 1 } } } - Filter by video count - media: { images: { width: { min: 1200, max: 1920 } } } - Filter by image width - media: { images: { height: { min: 800, max: 1080 } } } - Filter by image height - has_image: 1 - Articles with at least one image - has_video: 1 - Articles with at least one video - has_hq_images: 1 - Articles with high-quality images (width >= 1200px) - is_media_rich: 1 - Articles with both images and videos ### Source Filtering - source: { id: "314,315" } - Filter by source ID (up to 3) - source: { domain: "cnn.com,bbc.com" } - Filter by domain (up to 3) - source: { country: { code: "us,uk,de" } } - Filter by country (up to 3) - source: { rank: { opr: { min: 0.5, max: 0.9 } } } - OpenPageRank range (0-7) - source: { bias: "left,center,right" } - Filter by media bias (up to 3) - ignore: { source: { id: "315" } } - Exclude source IDs - ignore: { source: { domain: "example.com" } } - Exclude domains - ignore: { source: { country: { code: "fr" } } } - Exclude countries - ignore: { source: { bias: "left" } } - Exclude biases - is_premium_source: 1 - Premium sources (OPR >= 6) - is_verified_source: 1 - Verified sources (OPR >= 5, not duplicates) ### Date/Time Filtering - published_at: { start: "2024-01-01", end: "2024-01-31" } - Date range - published_at: "2024-09-26" - Specific date - Supported formats: YYYY-MM-DD, YYYY-MM-DDTHH:MM:SSZ, DD-MM-YYYY, RFC3339 - Max 31 days between start and end WHEN the same call also searches titles (title, ignore.title patterns or query); a wider range returns 400 ER0110. Without a title filter the range is unlimited. - An open-ended start ({ start: "2024-01-01" } with no end) runs to the current time, so with a title filter it exceeds the window too — always pair an archive start with an end date. ### Sorting - sort: { by: "published_at" | "created_at" | "source.rank.opr" | "read_time" | "sentiment.overall.score" | "sentiment.title.score" | "sentiment.body.score" | "media.images.count" | "media.videos.count" | "media.images.width.min" | "media.images.width.max" | "media.images.height.min" | "media.images.height.max" | "media_richness" | "relevance" | "engagement" | "quality" | "controversy" | "trust" } - sort: { order: "asc" | "desc" } - Advanced sorting: relevance (search ranking), engagement (viral potential), quality (editorial), controversy (polarization), trust (credibility) ### Pagination - page: 1 - Page number (default: 1) - per_page: 10 - Results per page (default: 10). One response carries at most 25 articles, so a larger per_page is clamped to 25 — use page to walk through more. ### Content Filters - is_duplicate: 0 - Exclude duplicates (0=unique, 1=include duplicates) - is_paywall: 0 - Exclude paywalled content (0=free, 1=paywall) - is_breaking: 1 - Breaking news only - read_time: { min: 1, max: 10 } - Filter by reading time (minutes) - is_long_read: 1 - Articles with read time >= 5 minutes - is_short_read: 1 - Articles with read time < 3 minutes ### Field Selection (fl) - Default (no fl): id, title, href, published_at, description, source.domain — body excluded - fl: "id,title,source.name,published_at" - Return only specific fields - fl: "title,href,body" - Ask for the full article text explicitly when you need to read it - Supports nested fields with dot notation: source.name, sentiment.overall.score ### Faceting - facet: true - Enable faceting - facet: { field: "source.id,language.id,sentiment.overall.polarity", limit: 20, mincount: 5 } - Supported facet fields: source.id, source.country.id, source.bias, category.id, topic.id, industry.id, language.id, author.id, sentiment.*.polarity, is_duplicate, is_free, is_important, media.images.count, media.videos.count, read_time, published.year, published.month, published.day_of_week, published.hour ### Range Faceting - facet: { range: { field: "published_at", start: "2024-01-01", end: "2024-12-31", gap: "1MONTH" } } - facet: { range: { field: "sentiment.overall.score", start: -1, end: 1, gap: 0.25 } } - Date gaps: 1HOUR, 1DAY, 1WEEK, 1MONTH, 1YEAR - Numeric gaps: 0.1, 0.25, 0.5, 1, 5, 10 ### Highlighting - hl: true - Enable highlighting - hl: { fl: "title,description,body", fragsize: 300, snippets: 5, tag: { pre: "<mark>", post: "</mark>" } } - Auto-expands search terms using synonyms and morphology QUERY BUILDING EXAMPLES: - Basic search: {title: "Bitcoin", language: {code: "en"}} - Sentiment analysis: {organization: {name: "Tesla"}, sentiment: {overall: {polarity: "positive"}}} - High-quality sources: {source: {rank: {opr: {min: 0.7}}}, is_verified_source: 1} - Date range (no title filter, so any width): {published_at: {start: "2024-01-01", end: "2024-12-31"}} - Title search over an archive month: {title: "Bitcoin", published_at: {start: "2024-01-01", end: "2024-01-31"}} - Multiple filters: {title: "AI", organization: {name: "Google,Microsoft"}, language: {code: "en"}, is_breaking: 1} - With media: {has_image: 1, media: {images: {count: {min: 2}}}} - Sorted by engagement: {sort: {by: "engagement", order: "desc"}} - With faceting: {facet: true, facet: {field: "source.id,language.id", limit: 10}} - With highlighting: {title: "innovation", hl: true, hl: {fl: "title,body"}} - Breaking news: {is_breaking: 1, sort: {by: "published_at"}} - Long-form quality: {is_long_read: 1, sort: {by: "quality", order: "desc"}}

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• toggle_favorite_storeA

  GotCashback MCPA

  Adds a store to, or removes it from, the signed-in user's favorite stores. Use it for 'add Best Buy to my favorites' or 'remove Nike from my favorites'. Needs the store_id: take it from an earlier result, or find it with get_stores_by_name (optionally with country_code). Requires OAuth sign-in with a GotCashback account; anonymous callers are prompted to authorize. All comparison tools work without signing in. Scope: 'favorites'.

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• toggle_favorite_storeA

  GotCashbackA

  Adds a store to, or removes it from, the signed-in user's favorite stores. Use it for 'add Best Buy to my favorites' or 'remove Nike from my favorites'. Needs the store_id: take it from an earlier result, or find it with get_stores_by_name (optionally with country_code). Requires OAuth sign-in with a GotCashback account; anonymous callers are prompted to authorize. All comparison tools work without signing in. Scope: 'favorites'.

  ConnectorNo auth

• list_brandsA

  TrakkrB

  List all brands you're tracking in Trakkr. Returns brand IDs and names — you'll need a brand_id for most other tools. On the hosted connector your brands are already named at the end of this description, so read the id from there instead of calling this. Call it only when that list is absent, truncated, or you need the extra fields below. Each brand also reports its primary 'location' (ISO-2 country code, e.g. 'GB'), plus 'location_region'/'location_city'. A null location means the brand isn't geo-pinned, which can skew competitor analysis toward global/US results — use set_brand_location to fix it. Args: brand_id: Optional. Filter to a single brand. include: Optional. Comma-separated extras: 'markets', 'aliases', 'profile' (the brand's own one-line description). These three are the only ones; an unrecognised value is dropped and reported, not treated as an error. Your brands: Nike = 9dab7956-c402-4e34-80d8-d9e111f3a6b0. Use these ids directly; no lookup call needed.

  ConnectorOAuth

• likeA

  uplikaA

  Like a post — or a comment: pass replyTo (a reply id from list_replies) to like that comment instead of the post. Idempotent: if it is already liked the call succeeds with already: true and nothing is toggled. There is no unlike. Works on Naver Blog and on Instagram accounts connected through Facebook; other channels answer not_supported. On Naver Blog the post can belong to another blog: pass its link and we act as the connected account. Naver ignores liking your own post, and that comes back as naver_not_allowed rather than a fake success. If the response carries `bridge`, `bridge.nextStep` says what is needed: `run_wake` means Chrome with the extension is closed, so tell the person `bridge.userMessage` with the `bridge.wake` command for their OS and retry once after they say Chrome is open; `ask_user_login` / `ask_user_update` mean tell the person `bridge.userMessage`. Do not retry more than twice.

  Connector

  Destructive

  No auth

• validate_claimA

  Nice GuidanceA

  "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

  ConnectorNo auth

• search_productA

  Bikefuchs — Bike Parts Price ComparisonA

  Search for bicycle parts, components, accessories, and cycling clothing by name, brand, or model number. Returns matching products sorted cheapest-first, each with its price, stock status, EAN barcode, and a direct purchase link. Supports DE and AT pricing. If you already have a product's EAN, use get_best_price instead.

  ConnectorNo auth

• entity_profileA

  Nice GuidanceA

  "Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when `value` was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. `sources_used` / `sources_failed` say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. `sources_skipped` is the third state: a leg we deliberately did NOT run, each entry carrying a `reason` token and a plain-English `detail` (the Purple Book is skipped for a filer SEC classifies outside the life-science SIC bands, since it lists only 351(a)/(k) biologics licence holders). Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit `notes` line, not a bare failure. `type` accepts "company" or "ticker" interchangeably — both take the same `value` shapes above.

  ConnectorNo auth

• scan_dependencyA

  Nice GuidanceA

  Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

  ConnectorNo auth

• watchlist_addA

  amazon-product-research-mcpA

  Create or add to a saved tracking list the user can monitor over time. list_type is one of asin, brand, seller, niche; name is the user's label for the list; items are the identifiers to track (ASINs, brand names, seller names, or niche keys). Captures a baseline of the current observed state so a later 'what changed' check can show new sellers and score moves. Use when the user says 'track these ASINs', 'add Nike to my brand watchlist', 'start monitoring ...'.

  ConnectorNo auth