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650,527 tools. Updated 2026-10-14 05:28

"AMG" matching MCP tools:

  • Keyword Research (marketplace term universe): search the FULL Top Search Terms store by phrase - not anchored to any ASIN. Filters: contains (up to 5 substrings, ANDed), department, rank_max, min_volume; period WEEK or MONTH (MONTH = monthly volumes); sort rank | volume | trend. Each term: search_frequency_rank, volume (amazon_sqp = TRUE Amazon volume where any account's SQP covers the term; estimated_from_rank with volume_band otherwise), rank_change vs ~4 periods back, and the top-3 clicked ASINs with click/conversion shares (the term's competitive landscape). The estimator block reports calibration health (pairs, typical error factor, band coverage). Use keyword_finder for ASIN-anchored lookups; this tool for term-first research. For the guided process start with keyword_opportunities.
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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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  • Create a throwaway sandbox wallet for this client. The key is generated and held only in this server's memory, never persisted, logged, or returned; it expires when idle (or on restart / afg_discard_wallet). Returns a wallet_handle and address to use with the other tools. Refused unless the API confirms a test-money mock ledger. Prefer your own wallet (afg_prepare_signed_request) if you have one. TEST MONEY ONLY: mock ledger, test tokens (tUSDC), no real funds.
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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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Matching MCP Servers

  • A
    license
    A
    quality
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    maintenance
    Enables text-only language models to understand images by forwarding image and prompt requests to vision model backends via MCP, returning descriptive text. Supports multiple OpenAI-compatible providers with task routing and safety checks.
    1
    MIT
  • A
    license
    A
    quality
    B
    maintenance
    Enables finding information online, reading web pages, finding images, capturing screenshots, and performing multi-source research with per-answer confidence.
    5
    AGPL 3.0

Matching MCP Connectors

  • Use your own trained text and picture classification models from any AI assistant.

  • Privacy-first, cookieless web analytics: traffic, sources, pages, goals, funnels and site setup.

  • 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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  • EXECUTE a pending proposal — call ONLY after you have rendered the full diff to the user as text (entities by name, current → proposed, reason) and they agreed. approved_summary must restate what they approved; it appears in the client's permission prompt, so a bare proposal_id is never shown alone. PARTIAL approval: when the user wants only SOME items, pass the rest as excluded_refs — same behavior as the dashboard's per-item checkboxes. Single-use. Expiry: 60 minutes for chat-staged proposals, 72 hours for workforce-staged ones - the proposal's own expires_at is authoritative.
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  • Opt in to (or out of) public names on https://afg.ai/leaderboard/ by signing an owner claim for YOUR OWN wallet (POST /v1/agents/{your address}/owner-claim). Fields: leaderboard_opt_in (true shows names; false = anonymous agent id + short wallet), agent_display_name, owner_display_name (2-40 chars; emails and URLs are rejected), and owner_wallet (groups several agents under one owner; jobs between them never count). An owner's public name is set by the owner_wallet signing this for itself. Self-asserted; never required for jobs. TEST MONEY ONLY: mock ledger, test tokens (tUSDC), no real funds.
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  • Train a new version of the model. Two steps: the first call changes nothing and returns what the run will use from the workspace's allowance plus a confirmation code; show that to the user, and only after they explicitly agree call again with that exact code. The user's yes is not the code. A code works once, for this model and these arguments, for 10 minutes.
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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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  • Report the freshness and health of the cross-chain program/token discovery pipeline: when the hourly cron last ran, how many programs/tokens each chain scan produced, and which scans failed. Use this to check whether discover_programs / search_tokens data is current before relying on it.
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  • Query keyword/target bid performance across Sponsored Products AND Sponsored Brands (rows carry ad_type SP/SB; SD targeting expressions live on ppc_query dataset sd_targets). Returns keywords with current bid, cost, sales, ACoS, clicks, and a recommendation hint (increase_bid, decrease_bid, pause_candidate, monitor). Supports metric threshold filters. NOTE: impressions here are AD impressions, not search volume — for organic search-query volume use brand_sqp. Default window ends 3 days back (newest days are attribution-incomplete); pass end_date to override for reporting.
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  • KW research FRONT DOOR - start here for any keyword question about a product (new or established). One call, five intent sections, every row with evidence + volume + a suggested next action: target_gaps (proven-relevant terms with no serving keyword target), discover (Amazon's own suggested keywords - v5 recs with themes, suggested bids and 30-day impression share - that the product has no footprint on), asin_targets (competitor ASINs from top-clicked co-occurrence + Amazon's product-targeting recs), organic_strength (terms organic is carrying while paid spend continues - bid-down test candidates), defend (brand-term coverage). Volumes everywhere: amazon_sqp = TRUE market volume, estimated_from_rank with volume_band otherwise. Primitives for raw exploration: keyword_finder (ASIN-anchored), keyword_research (term universe).
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  • Make a trained version the live one, used by fills, classify and the Prediction API. Two steps: the first call changes nothing and returns what will change plus a confirmation code; show that to the user, and only after they explicitly agree call again with that exact code. The user's yes is not the code. A code works once, for this model and these arguments, for 10 minutes.
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  • Retrain the model with the answers reviewed in the Review Queue, with the settings of the live version. Two steps, like train: the first call changes nothing and returns what the run will add and use from the allowance plus a confirmation code; only after the user explicitly agrees call again with that exact code. Publishing the new version stays with a Builder (publish).
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  • "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.
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  • 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.
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  • 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 the SAME shape as a populated one — release_count:0, releases:[] — plus empty_reason:"no_releases_in_window" and a `hint` telling you to widen, so you never branch on the response shape and never have to guess whether zero means "nothing is scheduled" or "the lookup failed". Econ releases especially cluster on specific dates each month, so a 48h window often straddles a dead stretch.
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  • Launch a memecoin on the aeX402 launchpad on Robinhood Chain (EVM 4663) for a FIXED pump.fun-style bonding curve (1B supply, starts 0.0001 USDG, graduates at 10,000 USDG raised, quoted in USDG). You supply only identity — name + ticker (+ optional description/image/links). DEFAULT (custodial): the aeX402 backend SIGNS + SUBMITS the createVPool tx for you, so a claude.ai user with NO WALLET can launch in one call; returns {ok,status,txHash,vpId}. Optionally pass `creator` (a 0x address) to record as the coin owner. Pass build:true instead to get an UNSIGNED tx {to,value,data} to submit yourself (non-custodial — that requires `creator` and makes you the on-chain creator). A custodial call SIMULATES first, so a failure burns no gas: status "gated" means createVPool reverts in simulation (cause not determined — likeliest are the ticker still being claimed by a bonding coin or this vpId being taken; see `detail`/`revertData`), and status "unreachable" means the chain could not be reached, which is retryable. The launchpad is NOT chain-gated: createVPool simulates clean from an arbitrary sender (measured 2026-09-25).
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  • Query the Hyperliquid Info API — the perps/spot order-book data layer (read-only, no auth). The 'type' field selects the query: meta (perps universe + leverage), l2Book (order book for a coin), allMids (mid prices), clearinghouseState (a user's positions/margin), spotMeta, meta+assetCtxs (mark/funding/OI), candleSnapshot, and more. For type:metaAndAssetCtxs specifically: the raw response is TWO PARALLEL top-level arrays (universe names, asset contexts) correlated only by array position — do not try to read/scan it directly for '200+ coins' style questions (funding-rate outliers, biggest open interest, highest volume). Pass rankBy instead: it joins the two arrays server-side, sorts, and returns only the top N — the correct way to answer 'what has the highest/most extreme funding/OI/volume right now'.
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