"Radar" matching MCP tools:

• radarA

  loophole-tapeA

  Free. The covered pump.fun mints under 30 s old ranked by calibrated rug-within-300s probability and the covered mints under 15 min (>= 5 buys) ranked by true-graduation probability, ten each, refreshed every 5 s, each with the paid check URL and tool. The free preview of the paid number. No arguments. Use it to find fresh mints worth a paid check; for a mint you already hold call check_pumpfun_risk.

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• demand-radarA

  SYNTHORA x402 Intelligence MeshB

  Demand Radar: signed x402 demand analytics measured on the mesh itself (real Caddy access logs, weekly aggregate): most-probed endpoints, unpaid 402 interest, 200 conversions, unique IPs, published prices. POST {"vista":"sin_pagar|compradas|nadie_toca|mirados","limit":<=25}. [x402: 0.25 USDC per call on Base; first 5 calls free]

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• insider-radarA

  SYNTHORA x402 Intelligence MeshB

  Insider-style trade detection on Polymarket: scans the latest large fills (min_usd, default 10000 USD) over lookback_hours (default 24) and flags wallets that are fresh (few lifetime trades), sized big vs the market 24h volume, or entered at extreme odds right before the market moved. Each alert carries wallet, market, size, tx hash, a 0-100 severity score and explicit reasons; approximations are declared in the methodology field. For prediction agents tracking informed money. No charge on servi [x402: 0.03 USDC per call on Base]

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• prelaunch-radar--new-shopify-stores-pre-launch-radarA

  New Shopify Stores RadarA

  This tool calls the Actor "prelaunch-radar/new-shopify-stores-pre-launch-radar" and retrieves its output results. Actor description: New Shopify stores, including pre-launch stores behind a password page, detected from public Certificate Transparency logs, with RDAP registration date, niche, country and catalog counts. Also checks any domain for Shopify. Flat JSON for AI agents and MCP. Zero personal data.

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  Destructive

  API key

• company-hiring-radarA

  Company Intelligence Tools — Zinin M2M HubA

  Pull every open role a company is hiring for from its public job board (Greenhouse, Lever, Ashby) and turn it into a buying/expansion signal: role count, which functions are growing (sales, engineering, marketing), remote share and what's new. No login, no scraping, no proxies. — $0.01/call, x402 (USDC on base).

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• company-hiring-radarA

  Company IntelA

  Pull every open role a company is hiring for from its public job board (Greenhouse, Lever, Ashby) and turn it into a buying/expansion signal: role count, which functions are growing (sales, engineering, marketing), remote share and what's new. No login, no scraping, no proxies. — $0.01/call, x402 (USDC on base).

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• token-launch-radarA

  Market and Regulatory Data Feeds — Zinin M2M HubB

  Look up any token contract address or ticker across DEXes and get liquidity, 24h volume, pair age, a 0-100 quality score and risk flags. Powered by DexScreener's public API. No key, no wallet, no on-chain node. Not financial advice. — $0.01/call, x402 (USDC on base).

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• token-launch-radarA

  Market DataB

  Look up any token contract address or ticker across DEXes and get liquidity, 24h volume, pair age, a 0-100 quality score and risk flags. Powered by DexScreener's public API. No key, no wallet, no on-chain node. Not financial advice. — $0.01/call, x402 (USDC on base).

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• apify-niche-demand-radarA

  Apify Market IntelligenceA

  Produce a current demand-direction snapshot for one declared niche using observed public Apify Store user metrics. Public Store API only, no LLM. — $0.05/call, x402 (USDC on base).

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• gov-tender-radarA

  Market and Regulatory Data Feeds — Zinin M2M HubB

  Search EU public-sector tenders (TED Europa) by keyword or CPV code, optionally filtered by buyer country. Returns tender title, buyer, country, CPV codes, deadline and notice URL. No API key, no browser — public TED search API only. — $0.02/call, x402 (USDC on base).

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• gov-tender-radarA

  Market DataB

  Search EU public-sector tenders (TED Europa) by keyword or CPV code, optionally filtered by buyer country. Returns tender title, buyer, country, CPV codes, deadline and notice URL. No API key, no browser — public TED search API only. — $0.02/call, x402 (USDC on base).

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• new-opening-permit-lead-radarA

  Lead IntelligenceA

  Detect commercial permit and license openings from five fixed Socrata datasets and return evidence-backed signals. — $0.03/call, x402 (USDC on base).

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• g2b-contract-demand-radarA

  Market and Regulatory Data Feeds — Zinin M2M HubB

  Convert official PPS contract aggregates into buyer-agency demand priorities. Unofficial, independent Actor; not affiliated with or endorsed by any named source publisher. — $0.05/call, x402 (USDC on base).

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• g2b-contract-demand-radarA

  Market DataB

  Convert official PPS contract aggregates into buyer-agency demand priorities. Unofficial, independent Actor; not affiliated with or endorsed by any named source publisher. — $0.05/call, x402 (USDC on base).

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• get_map_snapshotA

  Smarter WeatherA

  Render a weather map image for visual analysis. Simple form: pass `product` (a viz-catalog product_id like "mrms_qpe_01h_pass2_conus", "goes_truecolor_conus", "spc_day1_categorical", "hrrr_precip_hybrid_derived_conus" (future radar), "hrrr_subhourly_conus" (15-min Future Radar), "mrms_radar_nowcast_conus", "rtma_conus", "nbm_daily_temps", or "nexrad_l3:{SITE}:{PRODUCT}" for single-site radar, e.g. "nexrad_l3:TLX:N0B") plus a location and zoom (5=regional, 8=metro, 10=city). Composed form: pass `scene` -- a declarative scene document layering basemap + multiple weather products + active alerts + storm features + inline GeoJSON in one image (layers draw bottom-to-top, under basemap labels). Example scene: {"scene":"1.0","view":{"center":{"lat":43.8,"lon":-91.2},"zoom":8},"layers":[{"type":"weather","product":"goes_truecolor_conus"},{"type":"weather","product":"nexrad_l3:ARX:N0B"},{"type":"alerts","filter":{"events":["Tornado Warning"]},"onError":"skip"}]}. Alert filters (all optional, AND-combined): `ids` (specific alerts), `events`, `severities`, `minSeverity` (Extreme>Severe>Moderate>Minor>Unknown). Single-site radar keys: the address is `nexrad_l3:{SITE}:{KEY}` where KEY is `N{tilt}{measurement}` and tilt 0 is the 0.5 degree sweep -- N0B reflectivity (dBZ, where and how heavy), N0G base velocity (knots toward/away from the radar), N0S storm-relative velocity (storm motion removed, so a couplet is rotation rather than translation -- prefer it for rotation questions), N0C correlation coefficient (0-1, debris and hail), N0X differential reflectivity (dB). Legacy codes (N0V, N0R, N0Q) are accepted as aliases. Not every site produces every key; when a render reports which keys a site has, retry with one of those. Optional `time` (unix seconds): closest frame. Forecast (HRRR/nowcast/NBM) honors future times; analysis (MRMS/NEXRAD/RTMA/GOES) clamps to latest past. Pass `time` for future-radar asks — do not claim that capability is missing. Product ids must be real viz-catalog entries -- shorthand like "radar" or "reflectivity" is not one. Omit `product` for the default hybrid precip still. For Alaska and Hawaii prefer a local site or `mrms_precip_hybrid_derived_alaska` over CONUS mosaics, which do not cover them. Returns the rendered image plus per-layer resolved valid times.

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• team_profileA

  Cito APIA

  Team/org card: identity, roster, recent matches, and form/trends/radar when available. When to use: - Team page / "who is on this roster?" - Builder team screen sample Prefer over: separate roster + matches + detail via call_api. Do not use when: UFC fighters → player_profile; unknown name → resolve_entity first. Dota may return partial roster (API gap). Prefer slug for lol/cod; teamId for cs2. Parallel-safe: yes. Upstream cost: 2–4. Example: { "game": "lol", "slug": "t1", "recentLimit": 10 }

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• polymarket_arbitrageA

  RadarA

  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

  RadarA

  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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• add_radar_topicA

  VarynForgeA

  Add selected angles from a radar topic to the content plan as radar-born article suggestions (provenance preserved; exact-title duplicates skipped). Angles come from expand_radar_topic; call list_radar_topics first to get signalObservationId + topicIndex. Auto-creates the content plan when the project has none. Each added suggestion lands in generating_brief and its brief forges automatically (free tier: queues past the daily cap and forges at reset) — do not also call generate_article_brief. Returns the created suggestionIds.

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• polymarket_fill_riskA

  RadarA

  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

  RadarA

  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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