"IGN" matching MCP tools:

• compare_entities

  Data Gov In

  "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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• get_app_link

  Eveoy — Verified In-Store Foot Traffic ($24.99/customer)

  Get the link to download the Eveoy shopper app (iOS / Android). Use this when the user wants to: - Download or install the Eveoy app - Become an Eveoy shopper - Find the app store link Trigger phrases include: "get the eveoy app", "download eveoy", "how do I become a shopper", "app store link", "install the app". Returns: { url, platforms, notes }. Returns the canonical get-app page, which routes to the correct store per device. Do NOT use this for: brand/business questions (use ask_eveoy) or pricing (use get_pricing). Cost: free. Latency: <50ms. Read-only. Idempotent.

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• panorama_implantation_complet

  france-data-mcp

  Étude d'implantation labo en 1 appel (V0.23). Géocode l'adresse cible puis agrège EN PARALLÈLE 7 sections : `territoire` (densités PS commune vs national + établissements), `demande` (profil démographique du BASSIN — rayon — via profil_iris : âge, CSP, revenu pondéré), `concurrents` (labos FINESS), `pourvoyeurs` (MCO/EHPAD/SSR/dialyse — drivers écosystémiques), `prescripteurs` (médecins RPPS + IDEL Ameli), `cds` (centres de santé), `referentiels` (qualité couverture FINESS↔SIRENE). Remplace ~15 appels MCP individuels par 1. Renvoie des RÉSUMÉS (count / top-N / moyenne), JAMAIS de listes brutes. AUCUNE interprétation métier (pas de 'désert médical' ni de verdict GO/NO-GO) — le caller LLM applique sa grille. DÉGRADATION (lis `couverture` — 1 drapeau par section) : `"ok"` | `"partiel:<raison>"` | `"indisponible:<raison>"`. Si une source est down, SA section est flaggée et le RESTE est renvoyé — comble alors le trou via l'outil unitaire correspondant (etablissements_finess_in_radius, professionnels_rpps_in_radius, densite_sante, centres_sante_in_radius…). Échec d'ANCRAGE (géocodage KO / adresse douteuse / code INSEE indérivable) = rejet total (RangeError). Pièges internalisés : Paris/Lyon/Marseille basculés sur le département (`meta.plm_mode=true`) ; `prescripteurs` expose `precis_count` (PS géolocalisés à l'adresse, pas au centroïde commune) ; `cds` sans distance individuelle (centroïde commune). WORKFLOW : appelle CET outil pour DÉMARRER une étude, puis creuse les sections `partiel`/`indisponible` via les unitaires, puis `enrichir_concurrents` sur le top 3 de `concurrents.top`. Sources : IGN (géocodage), FINESS DREES, RPPS/ANS, Ameli/CNAM, INSEE/FILOSOFI, SIRENE/DINUM.

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• entity_profile

  Data Gov In

  "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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• deep_research

  Data Gov In

  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 1272 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 4,862 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 (record-level pipeworx:// when the source emits one, else source-level). "thorough" also returns contradictions[] flagging findings that disagree. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

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• polymarket_fill_risk

  Data Gov In

  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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• get_pricing

  Eveoy — Verified In-Store Foot Traffic ($24.99/customer)

  Compute the exact Eveoy price for a pilot. Pricing mirrors eveoy.com/order. Base: customers_per_location × locations × $24.99. Optional guaranteed purchase (guarantee_type "visit_purchase"): every shopper also buys your chosen SKU at your register — add the SKU price (in cents, tax included, $5–$100) plus a 7.5% platform fee on the SKU only; the item money rings back into your till. Optional shopper bonus ($20–$200 per shopper, 33% platform fee on the bonus only): every $20 unlocks +1 photo and +1 follow/like/comment set per shopper, each capped at +3. Total = units×2499 + round(units×sku×1.075) + round(units×bonus×1.33) cents — the same server-side math Stripe charges. The marketing-default $999 pilot is 40 customers at 1 location, visit-only. Floor 20/location, ceiling 1,000/location, locations cap 50. Use this when the user wants to: - Get a price for a specific shopper/customer count and location count ("price 200 shoppers across 3 stores") - Quote a pilot with a guaranteed purchase and/or a shopper bonus, with the full fee breakdown - Get a budget estimate or compare cost across pilot sizes - Confirm the per-shopper rate before booking Trigger phrases include: "how much does eveoy cost", "price for 500 shoppers", "what's a pilot cost with a guaranteed purchase", "what does the bonus cost", "eveoy pricing", "quote me a pilot for 100 shoppers in 4 stores". Returns: { customers_per_location, locations, total_customers, unit_price_usd, total_usd, formatted_total, ugc_photos, is_starter_tier, guarantee_type, top_sku_price_cents, shopper_bonus_cents, fee_breakdown { base_cents, sku_cents, bonus_cents }, bonus_tiers }. Reflects the exact math the eveoy.com/order backend applies; never returns a number the form would reject. Do NOT use this for: - General "what is eveoy" questions (use ask_eveoy) - Industries served (use list_industries) - Custom volume contracts beyond the 50-location ceiling — route the buyer to support@eveoy.com Cost: free. Latency: <100ms. Read-only. Idempotent. Deterministic.

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• list_industries

  Eveoy — Verified In-Store Foot Traffic ($24.99/customer)

  Return the list of industries Eveoy serves — 23+ B2C sectors across retail, food, beauty, hospitality, pets, and more. Use this when the user wants to: - Check whether Eveoy supports their vertical ("do you do coffee shops?") - See the full list of supported industries - Confirm an industry before pricing or booking a pilot Trigger phrases include: "what industries does eveoy support", "do you work with QSR", "list verticals", "is eveoy good for fitness studios", "what categories", "what sectors". Returns: { industries: string[], count: number, notes: string }. Each entry is a canonical sector name suitable for downstream use. Do NOT use this for: pricing (use get_pricing) or general Eveoy questions (use ask_eveoy). Cost: free. Latency: <100ms. Read-only. Cacheable. Deterministic.

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• book_demo

  Eveoy — Verified In-Store Foot Traffic ($24.99/customer)

  Get the link to book a live Eveoy demo, and flag the request to the Eveoy team. Use this when the user wants to: - Schedule a demo or walkthrough - Talk to the Eveoy team Trigger phrases include: "book a demo", "schedule a call", "talk to sales", "get a walkthrough". Returns: { url } — the Eveoy demo-booking page. Do NOT use this for: pricing (use get_pricing), buying (use start_checkout), or questions (use ask_eveoy). Cost: free. Latency: under 1s. Notifies the Eveoy team that a demo was requested.

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• capture_profile

  Eveoy — Verified In-Store Foot Traffic ($24.99/customer)

  Save the company an agent represents so Eveoy can tailor recommendations and have the team follow up. Records first-party business-contact details (no consumer data) as a lead. Use this when the user wants to: - Tell Eveoy which brand or company they represent - Get tailored pilot recommendations or a follow-up from the Eveoy team - Set things up before requesting a quote or starting an order Trigger phrases: "I represent <brand>", "set up my company", "we're a <sector> brand", "save our details", "have someone follow up". Returns: { ok, company, note } — confirmation the profile was captured for this session. Do NOT use this for: pricing (use get_pricing), buying (use start_checkout), or general questions (use ask_eveoy). This does not create an order or charge anything. Cost: free. Latency: under 1s. Captures a lead; safe to call again to update the details.

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• recent_changes

  Data Gov In

  "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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• pipeworx_feedback

  Data Gov In

  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). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. 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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• resolve_entity

  Data Gov In

  "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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• bet_research

  Data Gov In

  Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets the resolver considered), and suggestions[] (explicit re-query hints when the match is fuzzy) — ALWAYS inspect these before trusting the analysis block, because medium/low matches can still surface other fields. PARENT_EVENT EXTRACTOR: when the bet is one leg of a partition (Yankees WS, Romania election), result.parent_event{matched_candidate, top_legs_by_price[], partition_size, placeholders_filtered} gives you the peer prices in one place — that's the headline for elections/championships. NEWS FIELDS: news entries carry _fallback_attempted / _fallback_failed_reason / retry_after_sec when GDELT 429s and GNews backfill ran or failed. SAFETY: low-confidence resolutions short-circuit with status:"low_confidence_match" and suppress analysis fields so agents can't accidentally size on phantom matches. Closed/dead markets that ARE still indexed by Polymarket (yes_price≈0, no volume, no liquidity) return status:"market_closed_or_inactive" and skip fan-out. In practice resolved markets are usually de-indexed and instead surface via the low_confidence_match path above — both routes are BLOCKING, just different mechanisms. Wide-spread markets (>10pp) carry tradeability:"illiquid_wide_spread" + an explanatory note. RESOLUTION-RULE RISK: market.cancellation_rule parses the void/postponement settlement out of the resolution text — refund_50_50 (shares settle flat 50¢ on void; EV-material for any entry away from 50¢, with ev_impact quantified), resolves_no_on_cancel, resolves_yes_on_cancel, carries_to_reschedule, or mentioned_unclear. null means the description never mentions cancellation. Check this before sizing sports/esports/event-occurrence bets — audited arb-bot ledgers show flat-50¢ void settlements are a recurring pure-rules loss.

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• subscribe

  Data Gov In

  Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id. Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions). Supported types: "sec_8k" (8-K filings matching ticker + item codes — e.g. items:["5.02"] = officer change), "polymarket_edge" (Polymarket↔Kalshi cross-venue mispricings — params:{topic:"fed"}), "fred_series" (new FRED observations — params:{series_id:"UNRATE"}). Delivery channels: feed (always on — pull via recent_alerts or GET registry.pipeworx.io/alerts.json), and optionally email (set delivery:{email:"you@x.com"}) or sms (delivery:{sms:"+15551234567"} — phone must be verified at /account first; 10/day cap).

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• polymarket_edge_tracker

  Data Gov In

  Edge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers "how long has this edge existed and is it shrinking?" — a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take). Args: days (lookback, default 14, max 30), window (snapshot family, default "1wk"). RESPONSE: tracked[] = every opportunity in the LATEST snapshot with its full edge_pp_net time-series across prior snapshots, first_seen, trend (new | widening | stable | decaying) and decay_pp_per_day (both computed on |edge_pp_net| — the value itself is signed by trade direction, negative = SELL YES); expired[] = opportunities that appeared in earlier snapshots but are GONE from the latest (closed, resolved, or arbed away) with their lifespan_days — the median lifespan is your competition clock; snapshot_dates[] = which days actually have data (snapshots are written when polymarket_edges runs on a cache-miss, so gaps mean nobody scanned that day). LIMITS: history depth is bounded by the 60-day snapshot TTL and starts from when snapshotting was enabled; decay numbers come from daily closes of edge_pp_net (net of default slippage), not intraday.

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• start_checkout

  Eveoy — Verified In-Store Foot Traffic ($24.99/customer)

  Create an Eveoy checkout and return a payment link. Pricing mirrors the order page: $24.99 per verified customer base, plus two options — a guaranteed purchase (guarantee_type "visit_purchase": every shopper buys your chosen SKU at your register; you add the SKU price in cents, tax included, $5–$100, plus a 7.5% platform fee on the SKU only) and a shopper bonus ($20–$200 per shopper, 33% platform fee on the bonus only; every $20 = +1 photo and +1 social set per shopper, max +3 each). Omit guarantee_type for a visit-only order. The server recomputes the total — what get_pricing quotes is exactly what Stripe charges. Works for agents directly — no sign-in required. Use this when the user has decided to buy and confirmed the size: - They picked a customers-per-location count (and optionally locations, guarantee, SKU price, bonus) and want to pay - Trigger phrases: "buy a pilot", "start checkout", "place an order", "let's order 100 customers with a guaranteed purchase" Provide your_name, work_email, brand_website, and campaign_start_date (at least 14 days out) — or call capture_profile first and I will reuse your saved details, then I only need campaign_start_date. For a guaranteed purchase also provide top_sku_price_cents. Returns: { checkout_url, session_id, total, customers, guarantee_type, fee_breakdown } — pay on Stripe's hosted page; no charge until then. Do NOT use this for: price-only questions (use get_pricing), saving your company (use capture_profile), or order status (use check_order_status). Confirm the customer count, guarantee choice, and total with the user first. Cost: free to call. Latency: 2-5s. Creates a real checkout session and a CRM deal (no charge until the user pays). Confirm first.

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• get_case_studies

  Eveoy — Verified In-Store Foot Traffic ($24.99/customer)

  List Eveoy case studies and lookbooks — links to the full write-ups on eveoy.com's newsletter archive. Returns pointers, not article text: open a url to get the full experience (images, related links, and in-page options to book a demo or check out). Use this when the user wants to: - See proof, results, or success stories from real Eveoy campaigns - Browse case studies by industry, or the latest lookbook - Get a link to read a specific case study or the archive Trigger phrases include: "case studies", "success stories", "show me results", "do you have proof", "lookbook", "examples of campaigns". Returns: { archive_url, items: [{ kind, slug, title, url }], note }. Every url points back to eveoy.com. kind is one of case_study | lookbook | playbook. Do NOT use this for: pricing (use get_pricing), general questions (use ask_eveoy), or the live business directory (use search_directory). Cost: free. Latency: 1–2s (sitemap, cached 10 min). Read-only. Idempotent.

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• create_invoice

  Nigeria Invoices (FIRS / NRS MBS e-invoicing IRN)

  Sign and submit a Nigeria e-invoice to FIRS / NRS MBS — POST /invoice/sign. FIRS signs it server-side and returns the official IRN (Invoice Reference Number), CSID (Cryptographic Stamp Identifier) and QR code to print on the invoice. Bring your own credentials via headers x-firs-api-key AND x-firs-api-secret. The IRN format is InvoiceNumber-ServiceID-YYYYMMDD (e.g. ITW20853450-6997D6BB-20240703): pass irn directly if you already have it, otherwise this server builds it from invoice_number + service_id + issue_date. You send flat fields; the server builds the UBL-mapped JSON and computes VAT (7.5% Nigeria standard rate by default; override per line with vat_rate) into tax_total and legal_monetary_total. Recommended: call validate_invoice first.

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• polymarket_kalshi_spread

  Data Gov In

  Cross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says so. TWO MODES: (1) `topic` — 10 pre-mapped macro shortcuts ("fed", "btc", "cpi", "gdp", "sp500", "recession", "next_pope", "next_uk_pm", "next_israel_pm", "2028_president") auto-fetch the matching event on each venue. (2) explicit `kalshi_event_ticker` + `polymarket_event_slug` for custom pairings. RESPONSE: each venue's leg-by-leg prices (raw probability 0-1) plus matched spread[].top_spreads_pp (Kalshi − Polymarket) where the same outcome shows up on both sides. SAFETY FIELDS: compatibility_warning fires in two cases — (a) matched_pairs:0 with skipped_cross_type>0 means the venues frame the topic with non-equivalent bet shapes (e.g. Kalshi range_bucket point-in-time vs Polymarket cumulative_threshold touch-anywhere — no arb exists), (b) matched_pairs:0 with skipped_cross_type:0 and both venues >5 legs means the token-overlap matcher found nothing in common — events likely semantically unrelated despite the topic keyword. temporal_alignment{polymarket_month,kalshi_month,aligned} tells you whether the two events resolve in the same calendar period; aligned:false means spreads are mathematically meaningless across the temporal gap. skipped_cross_type / skipped_cross_subtype counters expose how many leg-pair comparisons were dropped (cross-type = metric_type mismatch like MoM vs YoY; cross-subtype = inequality mismatch like cum_ge vs cum_le). Real cross-venue spreads are rarer than the macro-shortcut list suggests — most pre-mapped topics return compatibility_warning today; pre-mapped ≠ tradeable.

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