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270,916 tools. Last updated 2026-07-07 23:08

"Star Trek" matching MCP tools:

  • Browse the Statistics Greenland (Grønlands Statistik) PxWeb subject tree under the /Greenland database. Empty path returns root folders (type 'l') and tables (type 't'); supply a sub-path like 'BE/BE01' to drill deeper. Table IDs carry a '.px' or '.PX' suffix — pass verbatim to table_meta or query_table.
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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).
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  • "What's the ticker for…" / "find the CIK for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" — resolve a user-spoken NAME to the canonical/official identifier other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (returns ticker + 10-digit CIK + company_name from SEC EDGAR + pipeworx://edgar/company/{cik} citation URI; accepts ticker, CIK, or company name as input — auto-disambiguated), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/{rxcui} citation; accepts brand or generic name). Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.
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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}. 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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  • Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).
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  • Pull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed — each carries source, citation_uri (pipeworx:// when available), and the raw event payload. Filter by type (e.g. "sec_8k") and/or since (ISO timestamp). Set mark_read:true to flag returned events read so the next call only shows newer ones. Polls work fine; the same feed is also at GET registry.pipeworx.io/alerts.json for scripts and dashboards.
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Matching MCP Servers

  • A
    license
    B
    quality
    D
    maintenance
    A comprehensive Model Context Protocol service providing zodiac information queries, horoscope analysis, and compatibility testing between star signs.
    Last updated
    7
    30
    2
    MIT
  • A
    license
    B
    quality
    F
    maintenance
    Enables users to query the Star Wars universe through the SWAPI (Star Wars API) directly in Claude Desktop. Supports searching for characters, planets, and films, as well as retrieving detailed information about the Star Wars saga.
    Last updated
    4
    15
    MIT

Matching MCP Connectors

  • 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 case-insensitive substring (matched on destination or name), `country` is an exact case-insensitive match, `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` (hotel grade, often null in the catalogue), and `themes` (curated theme labels decoded from the catalogue's thematics tags, e.g. ["pool", "luxury"]). 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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  • 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 1242 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 4,773 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. Expect 15-60s.
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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, news, GLEIF and returns: cik + company_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); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first if you only have a name).
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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).
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  • Multi-operator accommodation comparator for a geographic area against the user's stay parameters — dates, guest count, optional filters. Returns a ranked list of properties together with the booking sources that offer each one and, when dates are passed, their live availability and per-operator price for the requested window. Natural-language date references — "tonight", "this weekend", "next weekend", "the weekend of July 4", "Memorial Day weekend", "long weekend in May" — translate to concrete check_in / check_out values at the call site; concrete ISO dates also work. `user_country`, `currency`, and `language` carry the **user's** locale, not the destination's. IMPORTANT — currency: prices are returned in `currency` if you set it, otherwise in the currency derived from `user_country` (US→USD, CA→CAD, GB→GBP, euro-area→EUR); if you set NEITHER, prices default to **USD**, which may not be the user's currency. So whenever you know where the user is (or what currency they want), pass `user_country` and/or `currency` — do not rely on the default. Prices are never converted client-side; each offer is quoted by the operator in that currency. `user_country` and `language` also localize the booking link (`web_url`). The user's own residence/billing country is the right `user_country` (not the destination's), and their interface language the right `language`. Each result is shaped for downstream presentation without extra calls: - `location.lat` and `location.lon` carry per-property coordinates, suitable for plotting all results on a single map so the user can compare spatial alternatives at a glance. The map widget reads these fields directly from this response — no separate lookup needed for visualization. - `thumbnail_url` carries the property's first photo URL when available (null when no image is on file); useful for embedding inline or showing on the map alongside the pin. - `images` on search results is capped to the first photo to keep the comparison payload compact; each item has a `url` field, and `thumbnail_url` mirrors `images[0].url`. Call `get_property_details` for a single property to retrieve its full photo gallery. - `web_url` is a ready-to-open booking link for the property, already encoded with the user's check-in/check-out, language, currency, and guest count. Pass it to the user verbatim when they ask for a booking link — never reconstruct the URL from individual parameters, the query-string format is not guaranteed to match generic booking-URL conventions. - Price is **live and date-specific only**. There is no date-agnostic "from" figure: a meaningful price only exists for a concrete query (property + dates + occupancy). - `price` and `offers[]` — the **live quote for the requested dates**, populated only when dates were passed and the comparator confirmed availability. `offers[0]` is the curated best; each offer carries `amount` (total stay), `amount_per_night` (per-night), `currency`, `breakfast_included`, `refundable`, `rooms_left`, and `deeplink_url`. `price` mirrors `offers[0]`. - With no dates (or when nothing is available) `price` is null and `offers` is empty — surface the property without a price rather than inventing a starting figure. - `availability_status` per result encodes the live state: - `available` — bookable rooms confirmed at the operator level. `offers` and `price` carry the live date-specific quotes. Quote the rate via `offers[i].amount_per_night` (per-night) and `offers[i].amount` (total stay) and use the deeplinks for the booking handoff. - `unavailable` — no rooms reported for those dates. `offers` is empty and `price` is null (no price for these dates). Useful to decide whether to suggest alternate dates, drop the property from the recommendation, or offer it as a backup. - `unknown` — no dates were considered (request had no dates). `offers` is empty and `price` is null — no price signal without a dated query. Per-night vs total — never confuse them in the user-facing prose. `amount_per_night` is per-night; `amount` on each offer is the total stay (sum across nights, in `currency`). When quoting to the user, prefer phrasings like *"€X/night via Booking, breakfast included, €Y total for the stay"* over bare numbers — bare numbers without a unit get misread. - When dates are present and `available` properties are in the results, the rate can be quoted and `rooms_left` surfaces scarcity (low values like 1-3 are useful signals — "1 room left at $X on Booking" reads well). - When dates are present and ALL results are `unavailable`, that's the signal to say so explicitly to the user and offer to widen the dates, location, or filters. - `offers[]` is the per-operator breakdown for the requested dates: each entry includes `ota`, `amount`, `amount_per_night`, `currency`, `breakfast_included`, `refundable`, and a `deeplink_url`. The deeplink is a **BluePillow tracked-redirect URL** (bluepillow.com/…) that records the click for attribution and then forwards the user to the operator's booking page. Pass it to the user verbatim — never reconstruct it or replace it with a raw operator URL; our APIs never emit direct OTA links. `price` mirrors the curated best offer. When no dates were passed (or nothing is available) `offers` is an empty list and `price` is null — there is no price to show. - Free cancellation is a meaningful decision factor and surfaces proactively in the user-facing summary. When a property has `price.refundable=true` (or any `offers[i].refundable=true`), it reads naturally as a property feature: "Hotel X — $120/night, free cancellation available", or "Booking offers a refundable rate at $130 (vs $110 non-refundable)". Refundable rates let the user lock in a price now and adjust the booking later, which is often the differentiator between otherwise-similar properties. The same proactive surfacing applies to `breakfast_included` when it's true for some offers but not all. - Prices in `offers`/`price` reflect the requested dates and guests; with no dates there is no price. For a final bookable confirmation, the corresponding `deeplink_url` (or the property's `web_url`) is the canonical handoff — booking URLs are not reconstructed by hand. - All rating-like fields are on a 0-5 scale (Google Places-compatible): `rating`, `reviews_aggregate.score_0_5`, the per-OTA scores under `distribution_by_ota`, each `reviews_sample[*].score`, and the `filters.min_rating` input. A user asking "rating at least 8 out of 10" maps to `min_rating: 4.0`; "at least 4 stars on Google" maps to `min_rating: 4.0`. Note: `rating`, `stars`, and `rating_count` come from the comparator's list payload and **may be 0 or absent for some properties** even when the property has reviews or a star classification — this is a comparator list-payload limitation, not a data error. When those fields are 0/absent, or when the per-OTA review breakdown (`distribution_by_ota`) is needed, call `get_property_details` to get the fuller `reviews_aggregate`. On the search path, `reviews_aggregate` carries the top-line `score_0_5`, `rating_count` (reviews backing the score) and `comment_count` (readable review TEXTS available) when the comparator returned a non-zero review count; `distribution_by_ota` is always empty on this path (per-OTA breakdown requires `get_property_details`). `rating_count` and `comment_count` are DIFFERENT magnitudes — most guests leave a rating, far fewer write text. Quote `rating_count` for "how many reviewed it" and `comment_count` for "how many opinions you can actually read". - Pass `include=["reviews_sample"]` to attach a sample of up to 5 recent guest review texts per property. Useful when the user's question involves qualitative criteria that don't map to structured filters ("a place with excellent breakfast", "quiet area", "family-friendly atmosphere"); review texts can be searched textually to corroborate or rule out matches. For a DEEPER read on ONE specific property — more review texts (up to 20) or the per-OTA breakdown — call `get_property_details` with `include=["reviews_extended"]` (and/or `reviews_aggregate`). `comment_count` on each result tells you how many review texts exist, so you can decide whether escalating to the detail call is worth it. `filters.property_types`, `filters.amenities`, `filters.min_rating`, and `filters.price_max_eur` narrow on structured criteria first; review-based reasoning is one extra round-trip per page and is typically reserved for fallback. Location modes: - `coordinates`: when lat/lon is already known from world knowledge or a prior call in this session (default radius 5 km; widen up to 50 km for broader queries; beyond that `bbox` or a parent destination is the right shape). - `destination_id`: opaque id obtained from `resolve_destination`, passed verbatim — values are not constructed or guessed. - `bbox`: explicit map rectangle. Property type tokens (canonical): hotel, apartment, house, villa, bb, hostel, farmstay, holiday-home. Common multi-language synonyms map server-side to the canonical set. Amenities filter is set-AND — each result has ALL listed codes. Common codes: wi-fi, parking, pool, air-conditioning, kitchen, garden, pets-allowed, for-families, facilities-for-disabled, non-smoking-only. Results are cursor-paginated; the `next_cursor` from a previous response goes into `page.cursor` for the next page. `location.type=property_id` is not accepted here — `get_property_details` is the path for a known property.
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  • Delete STAS note events. For one specific note, pass external_ids with the exact note:YYYY-MM-DD:<slug> id; external_id as one string is accepted as a compatibility alias. Use oldest/newest window deletion only when replacing a whole note set in that date window. Do not delete user-created notes without a note: external_id. dry_run is required: false deletes, true previews only.
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  • "Find NBA player [name]" / "search NBA roster for [X]" / "is [player] in the NBA" — search NBA players by name on BallDontLie. Returns position, height, weight, college, country, draft info, and current team. NOTE: per-season averages (PPG/RPG/APG) and career stats are NOT in this response — those require BallDontLie's ALL-STAR tier ($9.99/mo at https://www.balldontlie.io/) via the /season_averages endpoint, which is not currently exposed by Pipeworx. Free-tier _apiKey works for this tool.
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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}. 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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  • Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).
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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 1242 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 4,773 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. Expect 15-60s.
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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 1242 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 4,773 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. Expect 15-60s.
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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 1242 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 4,773 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. Expect 15-60s.
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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).
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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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