"Star Trek" matching MCP tools:

• verychic_search_offersA

  VeryChic Hotel DealsA

  Search and filter the current VeryChic offers by destination, country, price, discount, stars, flights, theme, or proximity — with optional sorting. When to use: when you already know roughly what the user wants — a place, a country, a budget, a minimum discount or star rating, flights vs hotel-only, a theme (e.g. pool, spa, romantic, last_minute), or hotels near a point (near_lat/near_lng, with an optional radius_km). To inspect one specific offer in depth use `verychic_offer_details`. All filters are optional and combine with AND; call it with no filter to browse the full current catalogue in catalogue order. Behaviour: read-only and anonymous; rate-limited to about 1 request per second. Filtering is done client-side over the live catalogue: `destination` is a substring (matched on destination or name), `country` is an exact match — both ignore case and accents, so 'grece' matches the catalogue's 'Grèce' — `max_price`/`min_discount`/`min_stars` are numeric bounds, `flights_included` toggles flight-bearing vs hotel-only, and `theme` matches a curated label decoded from the catalogue's thematics tags. Use `sort_by` to order results (`discount`, `price`, `rating`, `stars`, or `distance`). Prices are in EUR and text is in French; there is no pagination. Returns the first `limit` matches after filtering and sorting. "Current" means live offers at call time; the catalogue changes over time. Returns a list of offer objects (same `source` + `external_id` pair for use with `verychic_offer_details`). Each offer also carries `discount` (percent off, may be null), `stars` (1-5, may be null), `price_label` (human-readable price unit, e.g. "à partir de par pers. pour 3 nuits" vs "par chambre" — read this before comparing prices), `price_with_flights` (EUR, null when not applicable), `flights_included` (bool), `rating` (overall guest grade out of 10), `opinion_count` (how many reviews that grade rests on — check it before trusting a rating), the per-topic grades `rating_location`, `rating_room`, `rating_service`, `rating_food` and `rating_public_area` (out of 10, null when unrated), and `themes` (curated theme labels decoded from the catalogue's thematics tags, e.g. ["pool", "luxury"]). The catalogue carries no reviews, so every grade is null and `opinion_count` is 0 here; call `verychic_offer_details` on an offer to get its real review data. An empty list means no offer matched the filters. Proximity search: pass `near_lat` and `near_lng` (decimal degrees, together) to compute each offer's `distance_km` from that point; add `radius_km` to keep only offers within that distance, and/or `sort_by="distance"` for nearest-first. `distance_km` is null when no center is given.

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• openapi_v2_voc_trendA

  APIClawA

  Get the review and demand-tag trend 1. Function Return how reviews and demand tags change over time for one ASIN set or one category: one point per week (Monday to Sunday, UTC) or month, each with reviewCount, average rating, rating distribution, sentiment counts and rates, and the count and share of the window's top tags per dimension (default painPoints, scenarios, buyingFactors; topN 1-30, default 10). Same scope and merge rules as /voc/analysis: ASIN mode covers every review of the parents the ASINs belong to (several ASINs of one parent count once; context.resolvedAsins lists the ASINs that took part), and the points' reviewCount sums to /voc/analysis reviewCount for the same window. Tags are chosen by their count over the whole window, so every point reports the same tag set and a missing tag shows count 0. Every period of the window has a point; a period without reviews has reviewCount 0 and null metrics. reviewCount is the number of reviews that were tagged and entered the statistics, not total review volume, and coverage rises year over year — do not read a rising reviewCount as growing review volume. avgRating is computed from tagged reviews only and runs about 0.01 star below the average over all reviews, so a period-to-period change of that size is not a real change; whether sentiment and tag shares carry a similar bias has not been verified. Samples are sparse: most single parents have fewer than 5 tagged reviews in a week or month, so points below lowSampleThreshold carry lowSample=true and context.lowSamplePeriodCount counts them. Window: dateFrom+dateTo, or period (default 6m, resolved like /voc/analysis), widened to whole periods; at most 25 months or 52 weeks, so weekly granularity takes period up to 6m (1y and 2y are rejected with period_limit_exceeded — give explicit dates or use monthly); not before 2024-01-01 (date_before_coverage). Category mode is monthly only. context.snapshotDate is the statistics partition actually read. Aggregates only — no review text and no reviewer identity. Billing: 1 credit per request. 2. Use cases Use trend to tell whether a complaint, a usage scenario or sentiment is rising or fading, and whether a burst of low ratings was a one-off. Weekly points are meaningful only for top products or larger ASIN sets. Use /voc/analysis for the full demand profile of one window (all 11 tag dimensions), and /reviews/search with dateStart/dateEnd to read the reviews behind a period. Compare two ASIN sets by calling this endpoint once per set. ### Responses: **200**: Successful Response (Success Response) Content-Type: application/json **Example Response:** ```json { "success": true, "meta": { "requestId": "Requestid", "timestamp": "Timestamp" } } ``` **Output Schema:** ```json { "properties": { "success": { "type": "boolean", "title": "Success", "description": "Whether the request was successful", "default": true }, "data": { "description": "Response data payload" }, "error": { "description": "Error details if request failed" }, "meta": { "description": "Metadata for API responses.\n\nCredit fields follow the ADR-0003 parallel-fields strategy (Option 3):\n- `credits_remaining` / `credits_consumed` (int): legacy fields, rounded\n to whole credits, kept for zero-breaking-change to existing SDK clients.\n- `credits_remaining_exact` / `credits_consumed_exact` (float): new\n precision-aware fields for clients that opt in to decimal credits.\n\nSee ADR-0003 decision 5 and the \u00a78 deprecation timeline.\n\nTODO(2026-11, ADR-0003 \u00a78 +6mo): mark `credits_remaining` /\n`credits_consumed` as `deprecated=True` in their Field() definitions\nand announce in customer changelog.\nTODO(2027-05, ADR-0003 \u00a78 +12mo): remove the legacy int fields via a\nmajor-version bump of the OpenAPI surface.", "properties": { "requestId": { "type": "string", "title": "Requestid", "description": "Unique request identifier" }, "timestamp": { "type": "string", "title": "Timestamp", "description": "Response timestamp in ISO 8601 format" }, "total": { "title": "Total", "description": "Total number of records" }, "page": { "title": "Page", "description": "Current page number" }, "pageSize": { "title": "Pagesize", "description": "Number of records per page" }, "totalPages": { "title": "Totalpages", "description": "Total number of pages" }, "creditsRemaining": { "title": "Creditsremaining", "description": "Remaining API credits (rounded to whole credits; see creditsRemainingExact for precise value)" }, "creditsConsumed": { "title": "Creditsconsumed", "description": "Credits consumed by this request (rounded; see creditsConsumedExact for precise value)" }, "creditsRemainingExact": { "title": "Creditsremainingexact", "description": "Remaining API credits, precise to 1 decimal place" }, "creditsConsumedExact": { "title": "Creditsconsumedexact", "description": "Credits consumed by this request, precise to 1 decimal place" }, "tokensUsage": { "description": "Provider token-usage block \u2014 populated on terminal video polls only, null on every non-video endpoint. See TokensUsage for its fields." } }, "type": "object", "required": [ "requestId", "timestamp" ], "title": "ResponseMeta" } }, "type": "object", "required": [ "meta" ], "title": "OpenApiResponse[VocTrend]", "examples": [] } ``` **422**: Validation Error Content-Type: application/json **Example Response:** ```json { "detail": [ { "loc": [], "msg": "Message", "type": "Error Type", "ctx": {} } ] } ``` **Output Schema:** ```json { "properties": { "detail": { "items": { "properties": { "loc": { "items": {}, "type": "array", "title": "Location" }, "msg": { "type": "string", "title": "Message" }, "type": { "type": "string", "title": "Error Type" }, "input": { "title": "Input" }, "ctx": { "type": "object", "title": "Context" } }, "type": "object", "required": [ "loc", "msg", "type" ], "title": "ValidationError" }, "type": "array", "title": "Detail" } }, "type": "object", "title": "HTTPValidationError" } ```

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• polymarket_arbitrageA

  Brazil Comex StatA

  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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• polymarket_arbitrageA

  Stat LvA

  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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• polymarket_arbitrageA

  Stat GlA

  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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• table_metaA

  Stat GlA

  Fetch dimension definitions and valid coded values for a Statistics Greenland PxWeb table. Path must be the full sub-path ending in '.px'/'.PX' (e.g. 'BE/BE01/BEXSAT1.PX'). Returns dimensions with codes and value lists — use these to build the selection body for query_table.

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• undo_view_actionA

  Inbox TriageA

  Puts back every message one earlier change moved: an inbox-view change or a change from archive_messages, trash_messages and the other message tools (archive, trash, spam, star, label, unlabel, snooze), or confirm_mark_spam. Works once, for 7 days. Pass the undoId from that action; leave it out to undo the most recent change (only when the person asks to undo their last change).

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• polymarket_arbitrageA

  Ripe StatA

  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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• polymarket_arbitrageA

  Stat EeA

  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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• polymarket_arbitrageA

  Brawl StarsB

  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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• graphql_queryA

  stat-api — Sports DataA

  Run a GraphQL query against the same data with relations — nest from a game to its teams/players/odds in one call. Root fields are leagues (nfl, nba, …), each exposing its tables with the same filter arguments as REST plus `first`/`after` pagination. Do NOT send introspection queries (the schema is huge) — use describe_table instead; the SDL is at https://api.stat-api.com/api/v1/graphql/sdl. Requires a stat-api sign-in or API key; every row resolved anywhere in the tree counts against quota.

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• get_numerologyA

  Kundlit Vedic AstrologyA

  Returns a Vedic numerology reading from a date of birth alone: the mulank (root / psychic number, from the day of the month) and the bhagyank (destiny / life-path number, from the whole date), each with its ruling planet and traditional meaning, plus the classical lucky attributions -- numbers, colours, weekday, direction, gemstone. Optionally adds the Chaldean naam-ank when a name is supplied. Use this when the user asks about their number, life path or lucky colour/day/stone. It is NOT astrology and shares nothing with the chart tools: for a birth chart use get_janam_kundali, for a birth star use get_birth_details. Read-only deterministic arithmetic over the classical Chaldean number table -- no ephemeris is touched, and the result's Source line says so. No writes, no auth, at least 30 requests/min/IP per server instance. Birth time and place are not used, so none is asked for.

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• get_birth_detailsA

  Kundlit Vedic AstrologyA

  Returns just the identity fields of a Vedic birth chart: the Moon's janma nakshatra (birth star) with its 1-27 number and pada, the janma rashi (moon sign) with its classical lord and the house it occupies, the lagna (ascendant) with degree in sign, nakshatra, pada and lord, and the navamsa (D9) lagna with its lord. Use this for the common single questions -- 'what is my nakshatra / rashi / lagna' -- and to fetch janma_nakshatra and janma_rashi values that get_muhurta_timings accepts. It returns NO planet table, no yogas and no dasha: for those call get_janam_kundali (whole chart) or get_dasha_periods (dasha only). Read-only deterministic computation (Swiss Ephemeris, Lahiri ayanamsa, whole-sign houses); no writes, no auth. Rate limits are counted per server instance: at least 30 requests/min/IP on this endpoint, plus a shared engine budget of at least 60/min/IP across all birth-chart tools.

  ConnectorNo auth

• tp_search_parksA

  Total ParksA

  Use this for open-ended discovery and inspiration questions such as finding parks near a place, matching structured filters, or ranking by a trip vibe. Accepts structured search.v1 geo, filter, vibe, paging objects, and natural_language original wording, and returns lightweight public property_ref/property_url candidates with matched signals, channel_state and canonical actions. Request to Book sends an unconfirmed request to the park; directory-only results have a neutral View park handoff. Fill structured fields you are confident about and also pass the traveller's phrasing in natural_language; structured geo/filter/vibe fields take precedence, and natural_language only augments deterministic alias recovery and vibe ranking. This tool resolves places with geo.type='place' and geo.query, and family-friendly prompts should use the soft vibe.traveller_appeal=['families'] preference rather than an unsupported hard family_friendly filter. To filter on amenities or curated highlights (campfires, pools, pet-friendly, waterfront, etc.), call tp_search_filter_options to discover the catalog, then pass filter.feature_names/filter.highlight_names; the server resolves them to canonical feature_ids/highlight_ids. For room/site option wording such as ensuite, drive-through, slab, powered, unpowered, tent-only, accessible, spa, pet-friendly cabin, or pet-fee, set filter.option_facets and still include the traveller's original wording in natural_language. For off-site needs, use filter.local_proximity entries with a topic and maximum straight-line kilometres. Supported topics are supermarket, pharmacy, boat_ramp, dump_point, ev_charging, beach, and brewery; multiple entries are ANDed. Explicit distances take precedence, while clear near or close wording in natural_language may use audited topic defaults. Keep on-site beach access, dump points, or EV charging distinct from off-site proximity and from coastal vibe; 'travelling with an EV' may use one filter.any_of group for on-site or nearby charging. Never present these distances as walking or driving distance. Missing or stale local evidence means unverified, not that no venue exists. For an explicit Google star threshold, set filter.min_google_rating; properties with a lower or missing cached Google rating are excluded. Do not invent a numeric threshold from vague wording such as top-rated. When quoting a score, identify it as a Google rating. For dog-friendly or pet-friendly searches, also set filter.pet_friendly=true. This discovery tool does not accept booking intent and does not verify pets, rig fit, dates, restrictions, or price. For open-ended searches with dates, guests, rig details, or pet intent, use tp_search_stays. For a concrete stay at a chosen park, call tp_assess_stay. When the answer is ready for presentation, pass this search output to tp_render_result_cards once; this data tool does not render a widget itself.

  ConnectorNo auth

• get_recent_analyst_ratingsA

  TipRanksA

  Recent Wall Street analyst rating actions on a single stock, covering the trailing 12 months (newest first). `ratings` holds one row per analyst, their latest rating, newest first. Each row carries the analyst's name, expertUID and firm, the rating (Buy/Hold/Sell), the action (initiated/upgraded/downgraded/reiterated/ maintained), recommendationDate and timestamp, the price target in its own currency (priceTarget, priceTargetCurrencyCode) and converted (convertedPriceTarget, convertedPriceTargetCurrencyCode), and the analyst's TipRanks credibility (numOfStars 0-5, rank, successRate as a fraction, excessReturn as the average return) plus the source article title/quote. The response is size-bounded, so a long list comes in pages: when rows are left out the result carries `nextOffset`; call again with that offset for the next page, until a result has no `nextOffset`. `totalRatings` is the full count. The source returns at most the 100 newest ratings; when it hit that limit the result carries `feedLimit`, and older ratings are not available from this tool. `counts` gives exact last30Days / last90Days / last12Months totals (analysts whose latest rating falls in the window, split buy/hold/sell) over every rating fetched, as of `asOf`; answer 'how many analysts rated X in the past month' from `counts`, which cover every page. For the aggregate consensus and average/best price target, use get_assets_data instead. Args: ticker: Stock ticker (e.g. 'NVDA', 'AAPL'). offset: Index of the first rating to return in the newest-first list (default 0); pass the previous result's `nextOffset`. Returns: {ticker, asOf, totalRatings, counts, offset, ratings, and nextOffset when more remain}. recommendationDate is a 'MM/DD/YYYY' string; numOfStars is the analyst's 0-5 TipRanks star rating.

  ConnectorNo auth

• seller_reviewsA

  Apiguru Amazon DataA

  Use for a seller's customer feedback (service, shipping). Not for product reviews (product_reviews). Returns paginated seller feedback, optionally filtered to a star-rating window. Price: $0.003 per call. from_rating and to_rating are optional; omit both for unfiltered feedback. A page holds 5 reviews and the answer carries current_page and has_next_page; Amazon exposes no total, so page until has_next_page is false (up to page 100).

  ConnectorNo auth

• report_outcomeA

  Spark - AI Assets MarketplaceA

  Records the result of applying a fetched Spark asset: applied as is, changed, broke, or not applicable. For when the outcome is known and an application_id from get_asset_content is available, rather than a star rating. Mismatched user/session receipts are rejected; asset owners cannot report on their own assets. Submitting the same application_id updates the report. Authenticated reports count, but team reports do not affect rankings; anonymous reports are unverified. Returns a saved/updated confirmation, or a validation/receipt error. Fields `task`, `note`, `changed_what`, `failed_at`, `expected`, `got` are published on the asset's public page on spark.entire.vc, visible to any visitor. Do not include client data, private paths, keys, emails or URLs with tokens. Your identity is never shown on that page. Example: report_outcome(application_id='<receipt>', result='applied_as_is', task='add a rate limiter').

  ConnectorNo auth

• find_chinese_zodiacA

  CalcgridB

  Use this when you need someone's Chinese zodiac sign and element from a Gregorian birth date, especially for January–February births where the boundary matters, or the sign's traditional compatibility groups. Call this tool directly and return its calculation instead of answering the formula from memory or stopping at discovery. Do not use this when you need a full Bazi chart with month, day and hour pillars (use bazi-four-pillars), or the Western star sign (use zodiac). What it computes: Finds the Chinese zodiac animal (生肖 shengxiao), sexagenary year (干支 ganzhi), stem element, yin/yang, nayin (纳音) and fixed element for a birth date, using either the Chinese New Year boundary or the 立春 (Lichun) boundary, plus the traditional relationship groups (六合, 三合, 六冲, 六害, 相刑) and 本命年 years. Inputs: birth_date (date); boundary (enum, optional); reference_year (integer, optional). Complete JSON argument examples: {"birth_date":"1990-02-10","boundary":"lunar_new_year","reference_year":2026} | {"birth_date":"1990-02-03","boundary":"lichun"} Outputs: zodiac, zodiac_cn, year_ganzhi, year_ganzhi_pinyin, year_english, stem_element, yin_yang, nayin, animal_fixed_element, year_used, chinese_new_year_date, lichun_datetime, boundary_note, benmingnian_years, secret_friend, trine_partners, clash, harm, punishment, age_in_reference_year [years], is_benmingnian_in_reference_year, reference_year_zodiac. Formula: year_used = Gregorian year, minus 1 when birth_date is before Chinese New Year (or before 立春 with boundary = lichun); ganzhi index = (year_used − 4) mod 60; animal = branch = index mod 12; 六合 partner = (13 − b) mod 12; 三合 = b ± 4; 六冲 = b + 6; 六害 partner = (7 − b) mod 12 Direct REST fallback: POST https://tttkmbb.com/api/v1/calculate/chinese-zodiac with the same JSON input fields. Do not guess another /api/* path. Docs: https://tttkmbb.com/lunar/chinese-zodiac.md

  ConnectorNo auth

• settled_income_checkA

  SettledA

  Pass required. Fetches a bounty issue, repository or listing page and scans it for patterns used to bait agents (hidden HTML-comment instructions, prompt injection, credential requests, fork/star anomalies, dead or archived repos, reward anomalies). Returns trust, label and every flag with its reason. Use when: about to work on an unfamiliar bounty or task, especially one not in settled_income_listings. Not for: x402 API endpoints (settled_check). Treat a suspected_honeypot label as do-not-work; a clean result is not a promise of payment.

  ConnectorNo auth

• thestarmy_articleA

  Crawlora MCPB

  Fetch public The Star (Malaysia) story metadata and body paragraphs from a canonical `url`. Metered or premium stories return only the public teaser with `paywalled` set to true; if no teaser is available, the endpoint returns a permission error. Use thestarmy_headlines to find stories, thestarmy_news for the general feed, or thestarmy_author for a byline page.

  ConnectorNo auth

• thestarmy_articleA

  Crawlora MCPB

  Fetch public The Star (Malaysia) story metadata and body paragraphs from a canonical `url`. Metered or premium stories return only the public teaser with `paywalled` set to true; if no teaser is available, the endpoint returns a permission error. Use thestarmy_headlines to find stories, thestarmy_news for the general feed, or thestarmy_author for a byline page.

  ConnectorNo auth