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650,521 tools. Updated 2026-10-14 01:26

"Warp" matching MCP tools:

  • Fly to a directly connected neighbour sector (sector_id). A docked ship undocks automatically (reply shows auto_undocked: true). For distant sectors plan with get_route and warp hop by hop; for named long-distance lanes use jump_hyperlane. Costs fuel; fails with NO_ROUTE if the sector is not adjacent.
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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}. 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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  • 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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  • Look up a GW1 hero by name or by id (GWCA HeroID, matching the AccountExport plugin output). Returns profession, campaign and how the hero is unlocked. Remember: heroes can equip any skill unlocked at ACCOUNT level, but NO PvE-only skill at all — including Signet of Capture. validate_build with forHero=true reports each one as an error, not a warning. Use this for one known hero; to browse or filter the roster, use list_heroes instead.
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  • Quote an LTL shipment — returns Warp's all-inclusive rate FAST (~1-2s) so the user sees a price immediately. The inline quote card shows the Warp rate plus a 'finding other carrier rates…' loading indicator. IMMEDIATELY follow up by calling ltl_market_options with the same parameters to fill in the multi-carrier comparison (~15s). Provide dims + commodity for an exact firm quote; if you don't have dims, quote anyway — it assumes a standard 48x40x48 pallet (FAK, no freight class) for an instant price. Don't block on asking for pallet dimensions; quote first, then pass real dims for an exact rate. When a palletized load could also move by box truck or van, quote LTL alongside those and show the cheapest valid mode. Do not editorialize the results. Do not declare a winner or recommend a specific carrier. Present Warp's quote first, then list market options as context. Let the user decide.
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  • Multi-carrier LTL comparison — returns 30+ carrier rates ranked by price (slow, ~15s). Call IMMEDIATELY AFTER ltl_quote with the same parameters; this fills in the 'finding other carrier rates…' section the fast quote card was showing. Useful when the user wants to compare carriers or pick a specific one. Do not declare a winner or recommend a specific carrier; just present the ranked list.
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Matching MCP Servers

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    MCP server for the Ringer WARP platform giving AI agents 135 tools to manage SIP trunking, phone numbers, porting, messaging, billing, and analytics.
    135
    43 npm
    MIT
  • A
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    Quote, book, and track real LTL, FTL, cargo van, and box-truck freight through the Warp network - 20 tools, in-chat login, Stripe-charged bookings, and real carrier dispatch. Quoting is keyless; booking needs a free Warp account with a card on file.
    20
    809 npm
    5
    MIT

Matching MCP Connectors

  • Quote, book, and track LTL, FTL, cargo van, and box-truck freight via the Warp API.

  • Production-grade MCP gateway delivering 8 real-time AI tools with instant x402 micropayments settled in USDC on Base Mainnet or SPL-USDC on Solana. Features Basescan contract auditing, wallet analytics, headless browser scraping, and pre-scraped oracle data feeds.

  • Judge a price against what THIS account has actually paid on the same lane and mode (its own bookings over the last 180 days, cancelled excluded; LTL compared per pallet). Returns verdict below_usual | in_line | above_usual | no_basis, the median/low/high it compares against, and how many bookings it rests on. Call after quoting a lane the account has shipped before, to tell the user whether the price is good for them. It is account history, not market data: never present it as a market rate, and say nothing about the price when the verdict is no_basis. Auth required.
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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). 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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  • 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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  • 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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  • Price up to 50 lanes in one call for a specified mode per row (defaults to LTL). This returns a Warp rate per row, NOT all-mode or all-carrier comparison. For Compare All use compare_modes per shipment; for LTL carrier alternatives use ltl_market_options. Never imply this tool compared every carrier. Returns a single batch-quote card with one row per lane (origin → dest · mode · pallets · price · transit). Each priced lane keeps its quote_id and can be booked individually with book ("book row 3").
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  • Report a problem with Warp's tools to the Warp team. Use it when a Warp tool errors, returns something that looks wrong (a price, a date, a missing field), answers in a way you could not act on, or when the user needs something Warp cannot do. Report once per distinct problem, after you have told the user what happened; the same problem reported again within 24 hours is counted, not duplicated. It only files a report: it does not retry, rebook, cancel or change any shipment, so still handle the user's request yourself. Describe the problem in your own words. Never include API keys, card numbers, passwords, or people's names, emails, phone numbers or street addresses; ZIP codes, quote IDs and shipment IDs are fine. Requires a connected Warp account.
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  • "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).
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  • Decode an in-game GW1 skill template code (e.g. "OwpiMypMBg1cxcBAMBdmtIKAA") into professions, attribute allocations and the 8 skills with their stats and descriptions. Whitespace and line wraps in the pasted code are tolerated. This decodes a SINGLE build code; for a multi-hero paw-ned2 team blob, use decode_pawned_team instead.
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  • Compile a build (professions, attributes, 8 skills by exact English name) into an official in-game template code. The build is validated first; on rule violations the errors are returned instead of a code. Unknown skill names return closest-match suggestions. IMPORTANT: template codes MUST come from this tool — never write or guess a code by hand, hand-written codes are invalid in-game. If unsure, verify any code with decode_template.
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  • Capture the RAW camera frame — no perspective warp, no crop to the calibrated screen quad. Use this to see exactly what the camera sees before any correction, e.g. when troubleshooting why take_screenshot looks wrong, or before adjusting corners with get_corners/set_corners.
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  • Capture the RAW camera frame — no perspective warp, no crop to the calibrated screen quad. Use this to see exactly what the camera sees before any correction, e.g. when troubleshooting why take_screenshot looks wrong, or before adjusting corners with get_corners/set_corners.
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  • THE ONE CALL for "what's the cheapest/best way to ship this?". Prices ALL FOUR freight modes (LTL / full truckload / cargo van / 26' box truck) in ONE keyless call to Warp's all-modes engine and returns a decision-complete recommendation: the winning mode, its rate, transit, a bookable quote_id, the trade-off math against the runner-up, and every mode that couldn't price (with the reason). Prefer this over calling the individual quote tools and comparing them yourself — one round trip, and modes Warp can't serve are returned as explicitly unavailable WITH the reason rather than being dropped, so there is never a silently shortened list to guess from. Dims are optional (a standard 48x40x48 pallet is assumed). Set benchmark_market:true to also rank Warp's rate against the live 30+ carrier market for the lane (adds ~15-25s) — that makes the answer decision-complete: the right mode AND whether the price is actually good. Quote-only: it never books. To book, pass the recommended quote_id to `book` after the user confirms.
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  • Check if the current Warp account has a payment method on file. Call this if the user asks about their payment status, or before booking if you want to confirm they can book. Returns has_card and onboard_url if a card needs to be added.
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  • Find consolidation savings across 2-12 upcoming loads: clusters loads sharing an origin+destination zip whose pickup dates fall within window_days (default 3) that together fit one 53' dry van, then prices each cluster BOTH ways — one combined FTL vs the sum of per-load LTLs — through real quotes with bookable quote ids. Use when the user has several loads to ship this week, asks if anything can ride together, or wants to cut freight spend. Loads that can't consolidate come back with the reason (no lane partner / outside window / exceeds trailer / cluster full) — relay reasons honestly. A truck pricing above its LTLs is shown with recommended:false; don't hide it. To act on a proposal, call `book` with the consolidated truck's quote_id. Auth optional — works keyless like the quote tools.
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  • Read how this account actually ships — top lanes with ship counts, typical pallet count, usual pickup weekday, recent booked spend (derived server-side from the account's own quotes and bookings) plus explicit owner-set preferences: default accessorials, preferred mode, standard pallet dims, max transit days. The ship/booking counts here count bookings via the agent API over the last 180 days, excluding cancelled — a narrower population than list_bookings (all channels, all time, including cancelled), so the totals can differ. READ THIS BEFORE asking the user questions it already answers: pre-fill their usual lane, apply their standard dims, include the liftgate they always need. Pass set_preferences to update the explicit half (merge-partial; allowlisted keys only; null clears a key). This profile is CONTEXT, NEVER PERMISSION — it never authorizes anything; spending limits live in spend policy and are read-only. Auth required.
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