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755,178 tools. Updated 2026-11-07 10:04

"Sky" matching MCP tools:

  • Planetary positions, Moon phase and the coming week's sky events for a date (?date=YYYY-MM-DD, today by default). Works without a key: try curl https://api.natalchart.ai/v1/sky Use it for where the planets are on a date, the Moon phase, or the coming retrogrades and ingresses, when no one's chart is involved.
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  • Recommend dark-sky sites near a location, ranked by whether the drive is worth it. Use for "where should I drive to stargaze near X". Sites are grouped by drivability (how far), then ranked by a drive-worth score combining the best weather window over the next 7 nights, how much darker the site is than the user's location, and drive time. Drive times are approximate (straight-line x 1.3), not routed. Args: lat: Latitude in decimal degrees (-90..90). lon: Longitude in decimal degrees (-180..180). limit: Max sites to return, 1-12 (default 5). Returns {ranking (methodology note), tiers:[{tier, label, hint, sites:[...]}], nearest_km, uncertainty_note, attribution} — sites grouped into drivability tiers. Include the methodology note when presenting — it explains the ordering honestly.
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  • Get the turning points between two dates — dasha changes and slow-planet ingresses. This is the two-clocks view: the dasha sequence counted from birth (which theme is switched on) against gochara, the real sky on that date (the trigger). Use it for "what happens in the next five years", "when does this period change", "when does Saturn cross my Moon". get_dasha_periods answers when one clock changes; this answers when both point at the same thing. Every date is exact, bisected against the ephemeris to the day — not a sampled approximation. Args: chart_id: Handle returned by create_chart. The birth data is reused; the range below is independent of the chart's transit_date. start_date: YYYY-MM-DD, inclusive. end_date: YYYY-MM-DD, after start_date. systems: Dasha systems to include — vimshottari, yogini, ashtottari, chara_jaimini. Defaults to all four. Antardasha changes are reported for vimshottari only; the rest give mahadashas. types: Restrict to dasha_change, ingress and/or sade_sati. Defaults to all three. include_samples: Add a periodic strip showing the running dasha chain and Saturn/Jupiter position between the events. Off by default and capped at 13 rows, one year of months — for one date in full detail use get_transits. The event list is shorter when it is on. step: Spacing of that strip — month, quarter or year. Ignored unless include_samples is set. Returns the events, the natal anchors they are counted from, and the standing Sade Sati state at start_date. A range with more turning points than fit in one response is truncated **explicitly**, with the date to resume from — it is never silently shortened.
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  • USE for one person's 90-year Long Range Journey theme card: birth_date + as_of_date (completed civil years; no wall clock). NOT Magian Age Spread board (get_age_spread / get_age_plate); NOT get_date sky/calendars. RETURNS compact cosmic birth card + long_range {age_civil, age_no (mod 90), age_aliases, theme_card, spread_offset, planetary_period}. One theme card only — never 90 ages. Feb-29 follows Postgres age(). Error as_of_before_birth when as_of_date < birth_date.
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  • USE for one person's 90-year Long Range Journey theme card: birth_date + as_of_date (completed civil years; no wall clock). NOT Magian Age Spread board (get_age_spread / get_age_plate); NOT get_date sky/calendars. RETURNS compact cosmic birth card + long_range {age_civil, age_no (mod 90), age_aliases, theme_card, spread_offset, planetary_period}. One theme card only — never 90 ages. Feb-29 follows Postgres age(). Error as_of_before_birth when as_of_date < birth_date.
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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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  • Factual sky for AIs: ISS, auroras, archives. Never confuses unexplained with extraterrestrial.

  • Solar installer for Munich and Rosenheim: 3D solar roof check, local subsidies, EEG feed-in, guide.

  • 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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  • Hybrid search — combines keyword + semantic search via RRF. Uses Reciprocal Rank Fusion (RRF) to merge exact-word results with meaning-based results. **This is the recommended tool for "discourses about X" / concept queries**, because the semantic side catches suttas that discuss a concept using different vocabulary (e.g. some mindfulness-of-breathing suttas use `assasati/passasati/dīghaṁ` instead of `ānāpānassati`). ⚠️ **Send the question and nothing else. Do not pad the query.** The whole string becomes one vector, so every word you add moves it. Appending your own candidate terms — synonyms, Pāli equivalents, a keyword list — searches for the blend, not for the question. Measured on `Buddha flies in the sky`: asking it plainly put the right passage at **rank 1** (3 relevant suttas). Appending three guessed Pāli terms (`buddha, agga, sagga`) pushed it down to **rank 5** and left only 1 — because `agga` (supreme) and `sagga` (heaven) drag the vector toward their own meanings. Have candidate terms worth searching? Give them their own `search_by_keyword` call and merge the two result lists. One tool asks what a passage means, the other asks where a word occurs; combined into a single string they cancel out. Rewriting the question to sound more canonical does not help either — the same query phrased as `rose into the air and flew like a bird` scored **zero** relevant hits. 💡 **Hints for the AI client:** - English queries usually work best (e.g. `mindfulness of breathing`) because the embedding model is multilingual but EN-primary. - Thai stop-word handling is weak. If a Thai query underperforms, the AI client should translate to Pāli/English first (see server instructions). - The default `limit=5` is often too small for a topic survey — use `limit=15-20` (max 20) for good coverage. - `language` only chooses what comes back; it does not change what matches or how results are ranked. - Looking for a concrete thing rather than a concept (an animal, an object, a place)? `search_by_keyword` over `language="english"` is often better. The translations are segment-aligned to the Pāli, so one search for `turtle` finds every turtle passage whatever the Pāli underneath says (`kacchapa`, `kumma`, `maṇḍūkakacchapa`), and each hit still carries its segment id. - Ranking is by similarity, NOT canonical importance — locus classicus suttas (e.g. MN118, DN22) may rank below smaller suttas that happen to use the exact vocabulary. Treat results as a starting point, then call `get_sutta` for the canonical references.
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  • Generate a cohesive SET of custom images on one SOLID-COLOR background or a TRANSPARENT one, each one a separate isolated subject sharing one visual style (icons, logos, game assets, UI elements, sprite/asset packs; transparent for images that drop onto any backdrop). The style parameter says how everything is drawn; the subjects parameter says what to draw; the background parameter is a color or "transparent". The style can also come from reference images via the styleReferences parameter - alone, or best combined with the style text (text plus references holds a style tightest). Returns a zip download URL. Cost: 1 credit per call. When a solid color fits the user's use case, it is the faster choice. Run generation calls sequentially, never in parallel - only one generation runs at a time per API key. For composed, full-bleed pictures (scenes, hero images, banners, posters, environments), use generate_illustration_set instead. For several images of ONE character (the same mascot, creature, person, or product in different poses or moods), use generate_character_set instead. For many different ideas from one brief, each in its own style (logo ideas, mascot concepts), use generate_concept_set instead. For ONE logo shown many ways, use generate_logo_variations instead. Output formats: PNG (lossless), JPEG, or WebP; JPEG has no alpha channel, so a set delivered transparent is PNG or WebP. Images can be delivered at a fixed width and height, at one fixed axis with the other hugging each subject, or at their native resolution. Size and quality considerations: Leaving width and height unset delivers images at their native resolution, unscaled under relative sizing except that a subject far larger than the rest is shrunk (fill sizing scales each subject to its frame), which produces the highest quality results and is recommended when images will be post-processed, composited, or resized downstream. On a solid background, native output dimensions vary between generations and track the subject count - roughly 650-950px per side for small sets, down to roughly 400-650 at the full 16; fewer subjects means larger native images. Fixing width and height (e.g. 512 and 512) guarantees consistent dimensions across all images and generations but applies resampling which may soften fine details; fixing one axis lets each image keep its subject's own proportion on the other. IMPORTANT - style and subject description rules for best results: The style applies to every image in the set, so it is what keeps them visually consistent. Put HOW the images are drawn (technique, palette, surface treatment) in the style, and make each subject description only about WHAT that one subject is, not how it looks. A quick test for any phrase: is it WHAT the subject is, or HOW it is drawn? HOW belongs in the style, shared across the set. - Get the style and the subject descriptions right with the user before you call. When their request puts how an image is drawn, a background, or a scene into a subject description (or names the subjects to draw in the style rather than as separate entries in the subjects list), fix it as you compose the call: routine moves of shared technique into the style you can just make, but when a change drops or alters something they explicitly asked for, tell them what you are adjusting and why first. Each call costs a credit, so it is worth getting this right up front rather than spending one on a framed or scene-filled result. - The style describes the visual treatment of the images (e.g. 'watercolor', 'pixel art', 'stained glass'). It must NOT mention background color, image count, layout, or sizing. - Do not list the subjects to draw in the style (e.g. 'illustrations of a fox, an owl, and a deer'); the style is only the shared visual treatment, and the subjects belong in the subjects list, one per entry. A category or theme word is fine (e.g. 'insect illustration'). - Do not put background color or background descriptions in the style or subject descriptions. - Avoid framing the style as a type of painted canvas ('oil painting', 'acrylic painting', 'gouache painting', 'pastel painting'). These tend to produce each image as a rectangular framed canvas with its own colored background, rather than an isolated subject. Prefer 'illustration' or a specific technique: 'watercolor illustration', 'pen-and-ink sketch', 'ink wash', 'relief-etching', 'pastel drawing', 'woodblock print'. - Avoid color-field or atmospheric phrasings in the style ('luminous backgrounds of violet, rose, and gold', 'set against jewel-tone fields', 'dreamlike rainbow atmosphere'). These instruct the image model to fill each image with colored atmosphere, producing framed compositions rather than isolated subjects. Describe only the linework, palette, and technique of the subjects themselves. - Do not describe an aged, weathered, cracked, or textured surface, ground, wall, panel, or paper that the whole artwork sits on ('on aged wood', 'cracked fresco wall', 'aged parchment surface'); name the art tradition(s) or style(s) instead ('fresco-style illustration'). Texture that belongs to a subject's own material is fine ('a weathered bronze shield', 'a cracked ceramic vase'). - No captions, labels, or annotations. Text that is part of the depicted object is fine (e.g. 'STOP' on a stop sign, 'EXIT' on an exit sign). - No grid lines, borders, frames, or separators. - No overlapping or collage-style arrangements. - Do not connect the subjects to each other or give them shared physical elements: no wires, cords, chains, ropes, ribbons, vines, or threads running between subjects, no frame or banner they share, no phrasing like 'connected by' or 'strung together', and no single continuous line or tube forming multiple subjects. Each subject must be drawable in complete isolation; connections inside one subject (a chain on an amulet, laces on a boot) are fine. - No dramatic/long drop shadows (subtle shadows are fine). - Subject descriptions should describe WHAT to depict, not where to position it. - Each image is ONE isolated subject, not a scene. Describe the subject with its pose or action and anything it directly holds, rides, or interacts with, but not the surrounding setting, environment, landscape, or sky. For composed scenes (a figure set within an environment), use generate_illustration_set instead. - Do not use size words (large, tiny, small, etc.) on the overall image subject (e.g. 'a large elephant', 'a tiny mouse') - all images are produced at the same size. Size words on details within the image are fine (e.g. 'a plate with a small insignia'). - Maximum 16 images per generation. Do not put the image count in the style. - Subjects must be distinct: entries that differ only in case, punctuation, or spacing count as the same subject and the call is rejected. Explicit filenames must be distinct too (a different extension alone is not distinct). - The style must actually describe a visual style, and each subject must name a drawable subject; text that does not is rejected. - Style description max length: 500 characters. Subject description max length: 200 characters each. - Size: width and height are separate parameters, each 256 to 512 pixels when given. Both given is an exact box; one given fixes that axis and the other hugs each subject (so images in the set differ on it, and it may fall below 256); neither given delivers native resolution, which is also the path to larger images. - Sizing: relative (the default) keeps the sizes the model gave the subjects in relation to one another, one scale for the whole set; fill scales each subject on its own to fill its frame less the margin, the icon-set convention, giving up relative size and enlarging subjects smaller than the frame (the result says by how much). Both can be changed later with edit_set. - Icons: the subject count sets how large a batch's icons can later be exported with export_set, crisp at every density: about a 136px base size with 13 to 16 subjects, 160 with 10 to 12, 180 with 7 to 9, 192 with 5 or 6, 256 with 4 or fewer. Every generation result states its batch's own crisp base size. - If the style check returns a suggested cleanup, show the user the specific changes and get their confirmation, then resubmit the approved prompt with validation set to "skip" so it generates exactly as approved (resubmitting without "skip" re-runs the check and may return further suggestions). See the validation parameter for when to use "skip" and "auto-apply". - To add more subjects to a set, call extend_image_set with the id of any batch in it, as often as the set needs. - If a "Rate limit exceeded" error is returned, wait the suggested number of seconds before retrying. Do not retry immediately. - Showing the result: as soon as the call returns, show the user every delivered image before you put them to use, then keep working without waiting for a reply (unless the user asked to review first), so they can ask for a change before anything is built on them. Show each image exactly as delivered, as its own file: the files are the product, and their exact size, margin and background are what the user asked for. Never present a new image made from them, even to check them; it hides all three. Files on disk or a link in your context are not delivery. Where you can open files on the user's machine, open the extracted index.html with the host's file opener; it shows each image at its delivered size with its dimensions. Where you can attach files to your reply, attach each delivered file as it is (attaching is not inspecting). Otherwise give the online viewer link and the zip link. Show every image unless the user asked for only some, and say in one line what you left out; if you can show none, say the set was made but you could not show it, and give the viewer link.
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  • Return recent coronal mass ejections (NASA DONKI) and the current Kp index. Use it to explain an aurora alert or space weather; for 'can I see the aurora from here' use aurora, which applies the latitude threshold. Live data through the Vigi-Sky API, cached 5 minutes; degrades to an explicit 'unavailable' when NASA does not answer.
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  • Historical odds for a SPECIFIC trip window at one resort — 'Whistler on March 27', 'Vail Jan 10–17'. From ~30 years of ERA5 monthly history it blends the window's months (weighted by days) into: the snow-day probability (the headline — for a short window how OFTEN it snows beats how MUCH falls in a season), window-scaled expected snowfall and typical base depth, the same window across the last 5 individual seasons, and the best nearby month (±1) by snow-day odds. Window max 31 days; dates are YYYY-MM-DD. This is HISTORY, not a forecast and not a prediction — for the next 14 days use get_weather_forecast, and for a whole-month question use get_monthly_snow. Windows with under 5 tracked seasons return insufficient rather than thin odds.
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  • USE for one person's Magian Age Spread board seat: birth_date + as_of_date (completed civil years; no wall clock). NOT the 90-year Long Range Journey theme card; NOT get_date sky/calendars; NOT two-person scoring. RETURNS compact cosmic birth card + age_spread {age_civil, age_no (mod 90), age_aliases, age_spread_pos, planet_row, planet_col, is_fixed_card}. Feb-29 follows Postgres age(). Error as_of_before_birth when as_of_date < birth_date.
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  • USE for one person's Magian Age Spread board seat: birth_date + as_of_date (completed civil years; no wall clock). NOT the 90-year Long Range Journey theme card; NOT get_date sky/calendars; NOT two-person scoring. RETURNS compact cosmic birth card + age_spread {age_civil, age_no (mod 90), age_aliases, age_spread_pos, planet_row, planet_col, is_fixed_card}. Feb-29 follows Postgres age(). Error as_of_before_birth when as_of_date < birth_date.
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  • Storm Watch: named multi-day storm systems the models are flagging days ahead, grouped as one event across every resort in the path and ranked biggest first. Use for "is a storm coming", "what's the next big system", "where will it dump this week". Each event carries its window, the forecast total per resort, and a confidence tier (watching / likely / locked). FORECAST, NOT OBSERVATION — nothing here has fallen yet; use get_snow_report or find_best_powder for snow that already has. Returns no events when nothing is being watched, which is the normal state outside a storm cycle.
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  • Every Western factor for one person on one day, in one shape, with no weighting. Each factor carries its raw value and range, its frame, the verdict Lilly's rules give, the rule's source, signification ids, the time it holds and whether it depends on the birth time. WORKFLOW: BEFORE: asterwise_get_western_natal. AFTER: asterwise_get_western_events — the same over a range, as a timeline. INPUT CONTRACT: birth — WesternBirthData (house_system used for natal_house). target_date (optional YYYY-MM-DD; defaults to today in the birth timezone). target_time (optional HH:MM; defaults to 12:00) — the local time in target_timezone the day's sky is read. target_timezone (optional IANA). DO NOT CONFUSE WITH: asterwise_get_western_transits_daily — the aspects only, with no rules. asterwise_get_western_horoscope — AI-written sun-sign text. Full output and error contract: https://docs.asterwise.com/mcp/tools/get-western-daily-factors/
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  • Every Western factor for one person on one day, in one shape, with no weighting. Each factor carries its raw value and range, its frame, the verdict Lilly's rules give, the rule's source, signification ids, the time it holds and whether it depends on the birth time. WORKFLOW: BEFORE: asterwise_get_western_natal. AFTER: asterwise_get_western_events — the same over a range, as a timeline. INPUT CONTRACT: birth — WesternBirthData (house_system used for natal_house). target_date (optional YYYY-MM-DD; defaults to today in the birth timezone). target_time (optional HH:MM; defaults to 12:00) — the local time in target_timezone the day's sky is read. target_timezone (optional IANA). DO NOT CONFUSE WITH: asterwise_get_western_transits_daily — the aspects only, with no rules. asterwise_get_western_horoscope — AI-written sun-sign text. Full output and error contract: https://docs.asterwise.com/mcp/tools/get-western-daily-factors/
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  • Generates the dataset's branded 1200×630 cover and attaches it: the page, the Community tile and the share card all wear it. Every dataset gets exactly one, generated once its description is written. Call it only for a dataset that has no cover (get_my_dataset says has_cover). A dataset that already has one is refused cover_exists, because a new cover REPLACES the current one everywhere, including a cover a person uploaded; pass replace: true only when the person has asked for a new cover. subject says in plain words what the image should depict — the place, the instrument, the phenomenon — and nothing about style, colour or size, which the service fixes. Leave it out to derive one from the dataset's name and description. Example: {"dataset_id": "…", "subject": "A weather station on the edge of Denver International Airport under a wide sky"}. COSTS MONEY: about $0.10 per image, and it is rate-limited per person and per workspace. A rate-limited or busy answer means do without a cover for now and say so; never retry it in a loop. To retry the SAME generation after an uncertain answer, send the idempotency_key it returned. Returns {dataset_id, cover_image_url, color, idempotency_key, visibility, public_projection_synced, dashboard_url}. The cover is already saved on the dataset; do not follow it with update_dataset. visibility is who can see the dataset: private (this workspace only), link (anyone with its link) or public (listed on Mostly Right), or null if unknown. public_projection_synced only says the public copy is current: it is true for a private dataset too and never means the dataset is public. Report visibility, not that flag. Change it only with set_dataset_visibility, and only when the person asks.
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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 1866 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 7,188 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
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  • 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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  • Claygent-style per-row research: answer ONE question about ONE company and return a JSON object matching your output_schema, with every field wrapped in its own evidence — {value, evidence_url, quote, status}. status is NEVER self-reported by the model: "found" means this code independently verified the model's quote is a VERBATIM substring of text actually fetched; "inferred" means a value was given but the quote could not be verified; "not_established" means no source answered the field, and its value is always null — never guessed, never a blended score. Sources: "web" fetches the company's own site (entity.domain required — homepage, /about, /leadership) via fetch_url; "catalog" checks entity_profile (SEC EDGAR; resolved:false for a private company is expected, not a failure); "both" (default) tries both. max_cost (USD, default 0.02, max 0.50) scales how many pages are fetched and refuses up front if it is too small to pay for even one extraction call (floor $0.002) — never silently overspends. A row where every source comes back empty costs nothing: every field is mechanically not_established with NO model call made. For a boolean/derived field (e.g. "does this company sell to healthcare"), the quote only has to SUPPORT the value — a descriptive sentence about customers/products counts, the source need not state the answer verbatim. leadership_page_status ("fetched"|"not_found"|"not_attempted", plus leadership_note) says plainly whether a leadership/team page was actually read, so a named-person field (head_of_sales) coming back not_established is distinguishable from "this company just doesn't publish one" vs. "we never checked". model_output_empty:true means the model returned nothing usable (a transport/budget problem) — every field is not_established for THAT reason, never read as 20 individual "no" judgments. Use with a fixed question + schema across many rows for "Claygent over N companies with a hard cost cap" (composes with batch callers). For picking from a fixed label list instead of extracting open fields, use classify_row (cheaper, one model call).
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  • Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
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