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646,809 tools. Updated 2026-10-07 11:57

"google trends" matching MCP tools:

  • This tool calls the Actor "meridianlabs/google-trends-scraper" and retrieves its output results. Actor description: Bulk Google Trends API and pytrends alternative. Each keyword lookup returns interest over time, interest by region, rising related searches and a plain-English summary: rising or falling, year on year, peak, seasonality. $5 per 1,000 keyword lookups; failed lookups are free.
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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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  • Live top-trending board for exactly one feed type. No keyword. For 'Amazon Best Sellers by Category', 'Google Trends by Category', 'Top Websites', 'Substack by Category', and 'TikTok Trending Hashtags by Category', category selects the board. Default sort is current rank. Use sort='rank_change' for climbers vs a prior snapshot (window 1d, 3d, 7d, 14d, or 30d). If the requested board is not in the database yet (for example a country that is not collected on the schedule), the board is fetched live. App Store Top Free, App Store Top Paid, and Google Play are live store boards, not keyword lookups. App history is available via get_growth or get_time_series with source app downloads or app rankings and an Android bundle ID. Live boards are only available here, not via get_time_series. Returns a plan-limit message when the request is rate limited or the monthly quota is used up.
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  • Live top-trending board for exactly one feed type. No keyword. For 'Amazon Best Sellers by Category', 'Google Trends by Category', 'Top Websites', 'Substack by Category', and 'TikTok Trending Hashtags by Category', category selects the board. Default sort is current rank. Use sort='rank_change' for climbers vs a prior snapshot (window 1d, 3d, 7d, 14d, or 30d). If the requested board is not in the database yet (for example a country that is not collected on the schedule), the board is fetched live. App Store Top Free, App Store Top Paid, and Google Play are live store boards, not keyword lookups. App history is available via get_growth or get_time_series with source app downloads or app rankings and an Android bundle ID. Live boards are only available here, not via get_time_series. Returns a plan-limit message when the request is rate limited or the monthly quota is used up.
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  • Live top-trending board for exactly one feed type. No keyword. For 'Amazon Best Sellers by Category', 'Google Trends by Category', 'Top Websites', 'Substack by Category', and 'TikTok Trending Hashtags by Category', category selects the board. Default sort is current rank. Use sort='rank_change' for climbers vs a prior snapshot (window 1d, 3d, 7d, 14d, or 30d). If the requested board is not in the database yet (for example a country that is not collected on the schedule), the board is fetched live. App Store Top Free, App Store Top Paid, and Google Play are live store boards, not keyword lookups. App history is available via get_growth or get_time_series with source app downloads or app rankings and an Android bundle ID. Live boards are only available here, not via get_time_series. Returns a plan-limit message when the request is rate limited or the monthly quota is used up.
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  • MCP server for google-trends

  • Google Trends: Search, Images, News, Shopping over time, growth metrics. Free key at trendsmcp.ai

  • Live top-trending board for exactly one feed type. No keyword. For 'Amazon Best Sellers by Category', 'Google Trends by Category', 'Top Websites', 'Substack by Category', and 'TikTok Trending Hashtags by Category', category selects the board. Default sort is current rank. Use sort='rank_change' for climbers vs a prior snapshot (window 1d, 3d, 7d, 14d, or 30d). If the requested board is not in the database yet (for example a country that is not collected on the schedule), the board is fetched live. App Store Top Free, App Store Top Paid, and Google Play are live store boards, not keyword lookups. App history is available via get_growth or get_time_series with source app downloads or app rankings and an Android bundle ID. Live boards are only available here, not via get_time_series. Returns a plan-limit message when the request is rate limited or the monthly quota is used up.
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  • Live top-trending board for exactly one feed type. No keyword. For 'Amazon Best Sellers by Category', 'Google Trends by Category', 'Top Websites', 'Substack by Category', and 'TikTok Trending Hashtags by Category', category selects the board. Default sort is current rank. Use sort='rank_change' for climbers vs a prior snapshot (window 1d, 3d, 7d, 14d, or 30d). If the requested board is not in the database yet (for example a country that is not collected on the schedule), the board is fetched live. App Store Top Free, App Store Top Paid, and Google Play are live store boards, not keyword lookups. App history is available via get_growth or get_time_series with source app downloads or app rankings and an Android bundle ID. Live boards are only available here, not via get_time_series. Returns a plan-limit message when the request is rate limited or the monthly quota is used up.
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  • Live top-trending board for exactly one feed type. No keyword. For 'Amazon Best Sellers by Category', 'Google Trends by Category', 'Top Websites', 'Substack by Category', and 'TikTok Trending Hashtags by Category', category selects the board. Default sort is current rank. Use sort='rank_change' for climbers vs a prior snapshot (window 1d, 3d, 7d, 14d, or 30d). If the requested board is not in the database yet (for example a country that is not collected on the schedule), the board is fetched live. App Store Top Free, App Store Top Paid, and Google Play are live store boards, not keyword lookups. App history is available via get_growth or get_time_series with source app downloads or app rankings and an Android bundle ID. Live boards are only available here, not via get_time_series. Returns a plan-limit message when the request is rate limited or the monthly quota is used up.
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  • Live top-trending board for exactly one feed type. No keyword. For 'Amazon Best Sellers by Category', 'Google Trends by Category', 'Top Websites', 'Substack by Category', and 'TikTok Trending Hashtags by Category', category selects the board. Default sort is current rank. Use sort='rank_change' for climbers vs a prior snapshot (window 1d, 3d, 7d, 14d, or 30d). If the requested board is not in the database yet (for example a country that is not collected on the schedule), the board is fetched live. App Store Top Free, App Store Top Paid, and Google Play are live store boards, not keyword lookups. App history is available via get_growth or get_time_series with source app downloads or app rankings and an Android bundle ID. Live boards are only available here, not via get_time_series. Returns a plan-limit message when the request is rate limited or the monthly quota is used up.
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  • Live top-trending board for exactly one feed type. No keyword. For 'Amazon Best Sellers by Category', 'Google Trends by Category', 'Top Websites', 'Substack by Category', and 'TikTok Trending Hashtags by Category', category selects the board. Default sort is current rank. Use sort='rank_change' for climbers vs a prior snapshot (window 1d, 3d, 7d, 14d, or 30d). If the requested board is not in the database yet (for example a country that is not collected on the schedule), the board is fetched live. App Store Top Free, App Store Top Paid, and Google Play are live store boards, not keyword lookups. App history is available via get_growth or get_time_series with source app downloads or app rankings and an Android bundle ID. Live boards are only available here, not via get_time_series. Returns a plan-limit message when the request is rate limited or the monthly quota is used up.
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  • Live top-trending board for exactly one feed type. No keyword. For 'Amazon Best Sellers by Category', 'Google Trends by Category', 'Top Websites', 'Substack by Category', and 'TikTok Trending Hashtags by Category', category selects the board. Default sort is current rank. Use sort='rank_change' for climbers vs a prior snapshot (window 1d, 3d, 7d, 14d, or 30d). If the requested board is not in the database yet (for example a country that is not collected on the schedule), the board is fetched live. App Store Top Free, App Store Top Paid, and Google Play are live store boards, not keyword lookups. App history is available via get_growth or get_time_series with source app downloads or app rankings and an Android bundle ID. Live boards are only available here, not via get_time_series. Returns a plan-limit message when the request is rate limited or the monthly quota is used up.
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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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  • Return how ONE page's Google Search performance changed over time. Pass start_date and end_date together as inclusive JST dates; comparison dates are not supported. Omitting both preserves the current now-minus-30-days rolling window, which may touch 31 JST dates because GSC filters by date inclusively. Explicit dates are converted to JST day boundaries for GSC rows. `granularity` defaults to day for windows up to about 35 days and week for longer windows; pass it to override. Buckets without GSC rows are omitted. Google-search only; data lags 1-2 days. site_id is optional when OAuth-authenticated. For cross-page snapshots use get_breakdown(dimension='page'); for query trends use get_keyword_performance.
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  • Research keyword demand: search volume, difficulty, CPC, and intent, plus keyword ideas, Google Trends interest and related queries, and Google Ads keyword performance. Volume and difficulty come from a live keyword vendor: a workspace without vendor credits gets an explanatory error with the path forward, never fabricated numbers. Use for what to target and how much demand exists; for who ranks today use serp_competitors. Already scoped to the connected workspace and its site; call directly, no domain or site parameter is needed. Cost: metrics and suggestions bill AI credits (live vendor data at actual cost); trends, related_queries, compare and performance are FREE (Google Trends / your connected Google Ads).
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  • Google Trends related topics for a keyword. Returns the topics and named entities associated with a keyword in a given country, as top topics scored 0-100 relative to each other and rising topics with percentage growth, each carrying its entity type. Google Trends topic discovery for market research and content planning. [$0.03/call]. Params — keyword: the search term; geo: ISO-3166 alpha-2 country code, e.g. US, GB, DE, JP (213 countries supported); timeframe: time window. Each response reports its own bucket size in `granularity`: past_30_days and past_90_days return a daily series, past_12_months and past_5_years weekly, windows under a day hourly (2004_present|past_12_months|past_30_days|past_4_hours|past_5_years|past_7_days|past_90_days|past_day|past_hour) Example params: {'keyword': 'bitcoin', 'geo': 'US'}
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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 1682 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 6,453 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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  • "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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  • List supported Google Maps place type values for search filters. Returns place_types as a string array. Use a value with place_type on google-maps.search or google-maps.nearby_search. Cost = 1 token.
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