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482,558 tools. Updated 2026-08-27 19:32

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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 1477 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 5,635 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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  • "What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.
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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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  • Get banking rules and requirements for a country. Returns IBAN requirements, SEPA membership, FATF listing status, national currency, account format specifications, and country-specific payment requirements (mandatory codes like KNP for Kazakhstan, Purpose of Payment for UAE, etc.). The `fatf_listing` block is the authoritative answer to "is this country grey-listed / black-listed / under FATF increased monitoring". Both FATF public lists are held in full, so a `not_listed` status is a positive determination and not missing data. Use it instead of training data for any FATF question, and note that the coarse `fatf` field is a separate, weaker signal about regional-body membership that says nothing about listing. Everything here is COUNTRY-level. `currency` is the country's national currency, not the denomination of any beneficiary account — never pair it with the payment currency to diagnose a currency mismatch (see `currency_note` in the response). Use this to check country-specific STP rules that could cause payment delays, repairs, or rejections (e.g., missing purpose codes, regulatory fields). If a country requires special payment codes, the response includes a payment_requirements block with field descriptions and categories. Use country_payment_codes to look up specific code values. Args: country_code: ISO 3166-1 alpha-2 code (e.g., "DE", "US", "KZ") Examples: country_banking_rules("DE") country_banking_rules("KZ") # includes KNP requirement info country_banking_rules("AE") # includes Purpose of Payment info
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  • "Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.
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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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  • BETA. Estimate what a cross-border payment will COST, split by WHO PAYS: the sending bank's published fee (the sender's side), what correspondents deduct in transit and what the beneficiary's own bank charges to credit it (the beneficiary's side), and what actually lands. This estimator is in BETA. Present every number as a typical case and a high case, never as a quote, and never let a user commit to a contractual amount on it. The payload carries beta=true while this holds. HOW TO READ THE ANSWER (relay these honestly): - `answered=false` means we REFUSED. The most common reason is that we hold no published tariff rule for the sending bank, in which case there is deliberately no total and no "recipient receives" figure. Say we do not know what that bank charges. Do NOT add up the parts yourself and present a total: treating the unknown fee as zero is the exact defect this tool was built to remove. - The correspondent fee is a RANGE (`p50` typical, `p90` high case), not a point. The spread is real: SWIFT tracking never reveals whether a payment was sent OUR, SHA or BEN, so a single cohort mixes all three. - THREE FEES, THREE DIFFERENT PAYERS, AND THEY ARE NOT INTERCHANGEABLE. `sending_fee` is billed to the SENDER by their own bank. `correspondent_fee` comes out of the payment in transit, so the BENEFICIARY bears it. `beneficiary_fee` is what the RECEIVING bank charges its own customer to credit the payment, so the beneficiary bears that too - and it is frequently the largest of the three (measured 2026-08-22 on one live corridor: 35.26 USD of sender-side cost against a 245.68 USD beneficiary bank fee). Never quote one of them as "the cost", and never call the beneficiary bank's fee a correspondent charge. `total` is the sending fee plus the transit deduction; `total_both_sides` adds the beneficiary bank's fee and is the all-in figure. - WHERE EACH NUMBER COMES FROM. The correspondent fee is OBSERVED, from payments we have tracked. `beneficiary_fee.source` is `tariff` (or `tariff_fallback`, see below) and never `observed`: a beneficiary bank deducts after the last bank that reports to GPI, so no tracking data can see it, and we read it off that bank's published incoming tariff instead. Say which is which when the user leans on a figure. - `beneficiary_fee.known=false` means WE HOLD NO INCOMING TARIFF for that bank (we hold one for roughly two thirds of beneficiary banks). Its charge is then missing from every figure, `total_both_sides` is null, and `recipient_receives.typical` is an UPPER BOUND - `recipient_receives.beneficiary_fee_known` says so. Do not fill that gap with a zero, a guess or a typical figure; say the receiving bank's own charge is not included and point the user at that bank's tariff. - `beneficiary_fee.applies=false` under OUR / OUR-OUR: the instruction says the sender covers every downstream charge, so the bank claims it back rather than taking it off the credit. The figure is reported but NOT subtracted. Our data ends before the account is credited, so we can neither confirm nor refute that it was honoured on a given payment. - `beneficiary_fee.segment` says which of the bank's incoming price lists was read. `segment_fallback=true` means the account type asked for had no usable schedule so the other one answered - which can only happen when `beneficiary_segment` was NOT supplied, i.e. when we were assuming the beneficiary matches the sender. Say that you assumed it. - `beneficiary_fee.reason='other_segment_only'` means you DID supply `beneficiary_segment`, and that bank publishes an incoming tariff for the other account type only (`beneficiary_fee.other_segment` names it). We decline to quote it. Do NOT report this as "we hold no tariff for that bank": we hold one, for a different kind of account. Tell the user which, because it is often the useful half of the answer. - `basis.n` is how many observed payments back the correspondent figure and `confidence` reflects it. At "low", present the range as rough. - `basis.level` says how specific the evidence is: `corridor` is this correspondent into this destination country, `correspondent` is that bank overall, and `currency` or `global` mean we hold nothing specific and are quoting a pool. Say so when it is a pool. - `assumptions` is a list of plain sentences explaining what shaped the number (SEPA, OUR honoured, PSD2, a modelled BEN uplift, a stale tariff). Relay the ones that matter to the user's question. - under OUR the correspondent leg carries `our_breach`: the measured share of OUR payments that lose a charge in transit anyway, and what that costs. p50 is 0 and p90 is that loss. Quote BOTH - "the beneficiary should receive the full amount, and in about 7% of the OUR payments we can follow end to end they do not" - never the p50 alone as a promise. - `chain.status` = `no_chain` means the pair settles on local rails (SEPA, domestic, same banking group) with NO correspondent deduction at all. IMPORTANT ON CHARGE TYPE: charge_type is an INPUT and is never inferred from tracking. OUR is a real instruction and usually holds - of 150 payments whose own MT103 declared OUR and which we could follow from the instructed amount to the settled one, 139 reached the beneficiary intact, against 6 of 52 under SHA. It is NOT a guarantee: the other 11 lost a flat correspondent charge in transit, and we find no evidence that this depends on the destination country or on a US correspondent being in the chain, so do not tell a user that OUR is safe everywhere except the US. BEN is materially more expensive than SHA and our high case models it rather than measuring it. If the user has not said which they will use, ask, or state which one you assumed. Pass `beneficiary_bic` whenever the user knows the receiving bank: without it there is no correspondent chain to price and no beneficiary bank to read a tariff from, so the answer is the sending fee alone and no total. `customer_segment` selects which side of the SENDING bank's published price list is read. It is not cosmetic: of 30 banks publishing both schedules, 9 of the 17 that answered on both quote a different fee, one of them 220 PLN for a company against free for a person. It defaults to `individual` here; pass `business` when the payer is a company, and say which you assumed. `beneficiary_segment` does the same for the RECEIVING side, which is a different bank's price list and not a restatement of the sender's. Of 120 banks publishing both schedules, 28 quote a different incoming fee (measured 2026-08-24), and it runs both ways: Hipotekarna banka (HBBAMEPG) credits a 100,000 EUR payment free of charge to a company and takes 0.1% of it from a person, while Nordea charges a person 60 SEK and a company 250. Omit it and we assume the beneficiary matches the sender, which is what this tool did before 2026-08-24 - so if you omit it, say you assumed it. Supply it when the user has told you who is being paid, and prefer asking over guessing when the amount makes the difference material. Available to any caller with an active subscription. The estimate itself costs no credits (tracking a payment does cost credits; never describe tracking as free). Args: bank_swift: Sending bank BIC (8 or 11 chars) amount: Transfer amount currency: 3-letter transfer currency charge_type: SHA (default), OUR or BEN. Ask the user rather than guessing beneficiary_bic: Receiving bank BIC; needed for a total channel: online | branch | mobile_app | any customer_segment: individual | business | financial_institution - the SENDER beneficiary_segment: individual | business | financial_institution - the party being PAID. Omitted, it mirrors customer_segment customer_sub_segment: standard | premium | private_banking | vip api_key: Optional API key (internal calls ride the MCP secret)
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  • Run a full pre-flight compliance check on an IBAN before sending a SEPA / cross-border payment. USE WHEN: the user is about to send a payment / payout / refund and wants to triage risk first, asks "is this IBAN safe to pay?", asks for sanctions screening, asks if a SEPA Instant transfer will succeed, or needs a numeric risk score for an internal payment-approval workflow. NOT A REGULATED AML/CFT PRODUCT — informational triage only. For regulated screening use Refinitiv, Acuris, or ComplyAdvantage. CHECKS: IBAN validity + sanctions (OFAC list, FATF jurisdictions) + SEPA Instant reachability + VoP (EU 2024/886) participant. RETURNS: the full validate enrichment plus a compliance object with risk_score (0-100, 0 = safest), risk_level (low/medium/elevated/high/critical), sanctions matched_lists + fatf_status, reachability, vop status, and flags[] (e.g. sanctioned_country, fatf_grey_list, emi_issuer, no_vop).
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  • "What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI when a ticker is implied; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under `unresolved` rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. 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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  • "Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.
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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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  • 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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  • 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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  • Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.
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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 1477 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 5,635 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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  • 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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  • Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.
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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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