Google Trends

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,431470 across 16801684 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 16801684 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,431470 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

 ⟨462 unchanged words⟩ (zero releases across every requested category) returns errorthe SAME shape as a populated one — release_count:0, releases:[] — plus empty_reason:"no_releases_in_window" withand a widen-the-window`hint` hinttelling ratheryou thanto anwiden, emptyso arrayyou —never econbranch on the response shape and never have to guess whether zero means "nothing is scheduled" or "the lookup failed". Econ releases especially cluster on specific dates each month ⟨9 unchanged words⟩ 

 ⟨7 unchanged words⟩ :"string"},"type":"array"},"empty_reason":{"description":"Present ONLY on a zero-release window: \"no_releases_in_window\". The window was queried and nothing is scheduled — this is an answer, not a failure.","type":"string"},"hint":{"description":"Present ONLY on a zero-release window: what to change (usually a wider `hours`).","type":"string"},"notes":{"items":{"type":"string" ⟨117 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,426431 across 16791680 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 16791680 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,426431 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

Beta version of ask_pipeworx: identical universal router (same 6,350426 tools, same arguments, same response shape)  ⟨68 unchanged words⟩ 

 ⟨6 unchanged words⟩  US unemployment rate?"}],"properties":{"ask":{"description":"Alias for question.","type":"string"},"input":{"description":"Alias for question.","type":"string"},"message":{"description":"Alias for question.","type":"string"},"prompt":{"description":"Alias for question.", ⟨25 unchanged words⟩  Accepts query, q, prompt, text, input, ask, message as aliases.","type":"string"}," ⟨11 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,350426 across 16591679 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨5 unchanged words⟩  fiscal 2023 revenue?"}],"properties":{"ask":{"description":"Alias for question.","type":"string"},"input":{"description":"Alias for question.","type":"string"},"message":{"description":"Alias for question.","type":"string"},"prompt":{"description":"Alias for question.", ⟨23 unchanged words⟩  Accepts query, q, prompt, text, input, ask, message as aliases.","type":"string"}," ⟨11 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 16591679 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,350426 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

 ⟨17 unchanged words⟩  the past year?"}],"properties":{"ask":{"description":"Alias for question.","type":"string"},"depth":{"description":"How many facets to ⟨43 unchanged words⟩ ,"thorough"],"type":"string"},"input":{"description":"Alias for question.","type":"string"},"message":{"description":"Alias for question.","type":"string"},"prompt":{"description":"Alias for question.","type":"string"},"q":{"description":"Alias for question.","type":"string"},"query":{"description":"Alias for question.","type":"string"},"question":{"description":"The research question, in natural language. Broad/multi-part is fine — decomposition is the point. Accepts query, q, prompt, text, input, ask, message as aliases.","type":"string"},"text":{"description":"Alias for question.","type":"string"}},"required": ⟨3 unchanged words⟩ 

{"examples":[{"key":"user_research_topic"}],"properties":{"k":{"description":"Alias for key.","type":"string"},"key":{"description":"Memory key to deletedelete. Accepts name, k, label as aliases.","type":"string"},"label":{"description":"Alias for key.","type":"string"},"name":{"description":"Alias for key.","type":"string"}},"required": ⟨3 unchanged words⟩ 

 ⟨2 unchanged words⟩ :"user_research_topic"},{}],"properties":{"k":{"description":"Alias for key.","type":"string"},"key":{"description":"Memory key to retrieve (omit to list all keys). Accepts name, k, label as aliases.","type":"string"},"label":{"description":"Alias for key.","type":"string"},"name":{"description":"Alias for key.","type":"string"}},"required": ⟨2 unchanged words⟩ 

 ⟨3 unchanged words⟩ "value":"AAPL"}],"properties":{"content":{"description":"Alias for value.","type":"string"},"data":{"description":"Alias for value.","type":"string"},"k":{"description":"Alias for key.","type":"string"},"key":{"description":"Memory key (e.g., \"subject_property\", \"target_ticker\", \"user_preference\"). Accepts name, k, label as aliases.","type":"string"},"label":{"description":"Alias for key.","type":"string"},"name":{"description":"Alias for key.","type":"string"},"text":{"description":"Alias for value.","type":"string"},"v":{"description":"Alias for value.","type":"string"},"value":{"description":"Value to store (any text — findings, addresses, preferences, notes). Accepts content, text, data, v as aliases; a non-string value is stored as JSON.","type":"string"}},"required": ⟨4 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,085350 across 15801659 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 15801659 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,085350 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

Beta version of ask_pipeworx: identical universal router (same 6,053085 tools, same arguments, same response shape)  ⟨68 unchanged words⟩ 

{"examples":[{"question":"What is the current US unemployment rate?"}],"properties":{"input":{"description":"Alias  ⟨55 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,053085 across 15711580 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

{"examples":[{"question":"What was Apple's fiscal 2023 revenue?"}],"properties":{"input":{"description":"Alias  ⟨53 unchanged words⟩ 

{"examples":[{"attribute":"revenue","company":"AAPL","period":{"fiscal_year":2023,"type":"annual"}}],"properties":{"as_of":{"description":"Past  ⟨363 unchanged words⟩ 

{"examples":[{"type":"company","values":["AAPL","MSFT"]}],"properties":{"type":{"description":"Entity  ⟨48 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 15711580 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,053085 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

{"examples":[{"depth":"quick","question":"What is the current US unemployment rate and how has it changed over the past year?"}],"properties":{"depth":{"description":"How  ⟨71 unchanged words⟩ 

 ⟨213 unchanged words⟩  real "no data", not a bug. `sources_skipped` is the third state: a leg we deliberately did NOT run, each entry carrying a `reason` token and a plain-English `detail` (the Purple Book is skipped for a filer SEC classifies outside the life-science SIC bands, since it lists only 351(a)/(k) biologics licence holders). Pass a ticker ("AAPL"), zero-padded CIK ⟨46 unchanged words⟩ 

{"examples":[{"type":"company","value":"AAPL"}],"properties":{"type":{"description":"\" ⟨62 unchanged words⟩ 

{"examples":[{"url":"https://pipeworx.io"}],"properties":{"max_links":{"description":"Maximum  ⟨37 unchanged words⟩ 

{"examples":[{"message":"Fleet #2184 smoke test: verifying pipeworx_feedback example call returns non-empty.","type":"other"}],"properties":{"claim_token":{"description":"Read  ⟨144 unchanged words⟩ 

{"examples":[{"window":"7d"}],"properties":{"window":{"description":"24h  ⟨25 unchanged words⟩ 

{"examples":[{"topic":"Fed rate decision"}],"properties":{"event":{"description":"Single-event  ⟨77 unchanged words⟩ 

{"examples":[{"days":14,"window":"1wk"}],"properties":{"days":{"description":"Lookback  ⟨31 unchanged words⟩ 

{"examples":[{"limit":5,"window":"1wk"}],"properties":{"category_filter":{"description":"Comma-separated  ⟨369 unchanged words⟩ 

{"examples":[{"market":"will-the-fed-increase-interest-rates-by-25-bps-after-the-december-2026-meeting-20260729232808636","side":"buy_yes","size_usd":1000}],"properties":{"event":{"description":"Basket  ⟨95 unchanged words⟩ 

{"examples":[{"since":"30d","type":"company","value":"AAPL"}],"properties":{"since":{"description":"Window  ⟨62 unchanged words⟩ 

{"examples":[{"key":"target_ticker","value":"AAPL"}],"properties":{"key":{"description":"Memory  ⟨29 unchanged words⟩ 

{"examples":[{"type":"company","value":"AAPL"}],"properties":{"type":{"description":"Entity  ⟨89 unchanged words⟩ 

{"examples":[{"entities":["Pipeworx","Zapier"]}],"properties":{"_apiKey":{"description":"Optional  ⟨97 unchanged words⟩ 

{"examples":[{"package":"left-pad"}],"properties":{"package":{"description":"npm  ⟨35 unchanged words⟩ 

{"examples":[{"query":"supply-chain risk","text":"Apple Inc. reported fiscal 2023 revenue of $383.285 billion, driven by strong iPhone and Services growth. Net income was $96.995 billion. The company faced supply-chain risk in China during the quarter."}],"properties":{"limit":{"description":"Max  ⟨55 unchanged words⟩ 

{"examples":[{"topic":"finance"}],"properties":{"topic":{"description":"Optional  ⟨28 unchanged words⟩ 

{"examples":[{"claim":"Apple's fiscal 2023 revenue was $383 billion"}],"properties":{"claim":{"description":"Natural-language  ⟨67 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,045053 across 1571 sources, fills arguments, fetches the ⟨87 unchanged words⟩ 

 ⟨69 unchanged words⟩ routes each to the right one of 6,045053 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

 ⟨221 unchanged words⟩ query ran and why that event won). Fleet #2064: when two Polymarket candidates tie on resolution time `polymarket_selected_by` now SAYS so, names every tied slug, names the tie-break that actually decided it (the candidate whose metric_type matches the Kalshi series, else lexicographic slug order), and states whether the winner's metric matches the Kalshi series — it used to assert "picked the soonest-resolving" byte-identically on calls that returned DIFFERENT events, because the tie was settled by upstream fetch arrival order. RESPONSE: each venue's leg-by-leg prices (raw probability  ⟨52 unchanged words⟩  probably not the same question), temporal_mismatch (theythe resolvetwo inevents are about different SUBJECT months — e.g. Kalshi "CPI in October" vs Polymarket "September Inflation"), temporal_alignment_unknown (the resolutionsubject month could not be parsed on one or both sides — NOT the same as confirmed-aligned; check each event's close/strike dateevent yourself), non_equivalent_bet_shapes, no_candidate_pairs, unclassified_legs_excluded, pairing_unverified  ⟨67 unchanged words⟩ ,aligned} tells you whether the two events resolveare inabout the same SUBJECT calendar period, in EITHER mode;mode — this is the period the question is ABOUT (e.g. "September" for a CPI release that settles in October), not necessarily when either side settles; null means it could not be computed (see temporal_alignment_unknown), not that the two sides align. Fleet #2062: this used to compare Polymarket's settlement date against Kalshi's subject month and call a match — fixed to compare subject month to subject month on both sides. FEES: every top_spreads_pp and low_confidence_pairs[] row  ⟨325 unchanged words⟩