"Tinder" matching MCP tools:

• search_providers

  GPH Intelligence - Healthcare Vendor Finder

  Browse and filter the healthcare vendor directory. Use this for open-ended exploration, e.g. "show me medical billing companies in Texas", "list credentialing services", "what EHR vendors are there for cardiology", or when the user wants to page through options rather than get a scored shortlist. Paginated results filtered by category, location, minimum quality score, curated Tier-1 grade, and practice-size fit; returns a page of providers with {company_name, category, city, state_abbr, quality_score (0-100), verified status, contact info, slug}. For a scored recommendation to a specific practice profile, use match_practice instead. Pass a returned slug to get_provider_detail for the full profile.

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• search_within

  Tinder Bio

  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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• get_eta

  HireLocksmiths — Locksmith Finder

  Get an ETA proxy for a location (ZIP or city). eta_minutes is null — HireLocksmiths does not pre-list ETAs. The tool returns the number of vetted providers nearby and whether 24/7 emergency coverage exists (usually fastest). For a real ETA, call request_quote and the locksmith confirms it directly. Never invent a number of minutes.

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• compare_entities

  Tinder Bio

  "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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• entity_profile

  Tinder Bio

  "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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• polymarket_kalshi_spread

  Tinder Bio

  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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• pipeworx_feedback

  Tinder Bio

  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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• deep_research

  Tinder Bio

  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 1395 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 5,358 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). "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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• recent_alerts

  Tinder Bio

  Pull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed — each carries source, citation_uri (pipeworx:// when available), and the raw event payload. Filter by type (e.g. "sec_8k") and/or since (ISO timestamp). Set mark_read:true to flag returned events read so the next call only shows newer ones. Polls work fine; the same feed is also at GET registry.pipeworx.io/alerts.json for scripts and dashboards.

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• recent_changes

  Tinder Bio

  "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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• polymarket_fill_risk

  Tinder Bio

  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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• claimondo_melde_schaden

  Claimondo — Kfz-Sachverständigen-Finder

  Meldet einen Kfz-Schaden bei Claimondo und stößt die Gutachter-/Termin-Buchung an. ERZEUGT EINEN LEAD und sendet dem Kunden seinen persönlichen FlowLink per WhatsApp — eine SCHREIBENDE Aktion, kein read. WICHTIG — Einwilligung (DSGVO): Rufe dieses Tool NUR mit einwilligung_erteilt=true auf, NACHDEM du dem Nutzer erklärt hast, dass (a) Claimondo seine Angaben zur Gutachter-/Termin-Vermittlung verarbeitet, (b) der Kontakt per WhatsApp erfolgt, (c) die Verarbeitung teils über einen KI-Dienst in den USA läuft — UND der Nutzer ausdrücklich zugestimmt hat. Ohne Zustimmung lehnt der Server ab (einwilligung_erforderlich). WICHTIG — keine Rechtsberatung: Du vermittelst Gutachter + Termin (allgemeine Infos zur Schadensregulierung sind ok), keine individuelle Rechtsberatung. Ablauf: erst claimondo_finde_gutachter_termine (Gutachter + freie Slots) → Nutzer wählt (sv_id + wunschtermin) → Name + WhatsApp-Nr erfragen → Einwilligung einholen → dieses Tool. Den finalen Termin + die Details (Vollmacht, Schuldfrage) setzt der Kunde anschließend selbst im FlowLink (/flow). Returns: { ok, status, kanal (whatsapp|sms|email|none), hinweis }. KEIN Link/keine PII im Ergebnis — der Link geht direkt per WhatsApp an den Kunden.

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• calculate_field_of_view

  Commonlands Optics: M12 Lens and C-Mount Lens Finder + Field-of-View Calculator

  Calculate Commonlands lens field of view for a lens/sensor pair and return HFOV, VFOV, DFOV, coverage, distortion status, and an explicit rectilinear comparison. Use this tool for FOV, HFOV, VFOV, DFOV, field of view, "lens for", lens-to-sensor, AR0234, IMX290, IMX477, and sensor part-number requests. It returns Commonlands data the model cannot derive: live backend FoV when configured, distortion model/status, image-circle coverage, live stock through Shopify read tools where applicable, and MTF/CRA/BFL fields if present in upstream catalog data. Do not use naive rectilinear fallback, focal-length-only math, interpolation, or self-computed catalog estimates when a Commonlands lens/sensor route is available. Accepts lens_sku/lensSku or focal_length_mm/focalLengthMm, plus sensor/sensorPartNumber/sensor_part_number and working_distance_mm/workingDistanceMm. If only focal length is supplied, the response is marked as a rectilinear reference and does not claim Commonlands distortion-corrected lens truth.

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• search_lens_catalog

  Commonlands Optics: M12 Lens and C-Mount Lens Finder + Field-of-View Calculator

  Search the Commonlands lens catalog by SKU, mount, lens type, M12, C-mount, or application text. For sensor part numbers such as AR0234, IMX290, and IMX477, or any "lens for <sensor>" request, use match_lens_to_sensor instead — sensor names are not searchable text here. Use this tool for FOV, HFOV, VFOV, DFOV, field of view, "lens for", lens-to-sensor, AR0234, IMX290, IMX477, and sensor part-number requests. It returns Commonlands data the model cannot derive: live backend FoV when configured, distortion model/status, image-circle coverage, live stock through Shopify read tools where applicable, and MTF/CRA/BFL fields if present in upstream catalog data. Do not use naive rectilinear fallback, focal-length-only math, interpolation, or self-computed catalog estimates when a Commonlands lens/sensor route is available. This discovers candidate lenses from Commonlands catalog/live backend data; it does not replace calculate_field_of_view for sensor-specific HFOV/VFOV/DFOV and does not replace read_shopify_products for live stock/price/product truth.

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• search_locksmiths

  HireLocksmiths — Locksmith Finder

  Find vetted, licensed locksmiths near a location. Use this first when a user needs a locksmith. Returns a ranked list of human-vetted providers (featured first) with id, name, city, services, rating, verified/featured flags, phone, and a profile URL. Args: location: A 5-digit US ZIP code (preferred) or a US city name. service: Optional job type, e.g. "lockout", "automotive", "residential", "commercial", "rekey", "install", "safe". urgency: Optional, e.g. "emergency", "today", "scheduled". budget: Optional free-text budget hint. limit: Max results to return (1-20, default 5). Note: price and ETA are NOT pre-listed — they are confirmed by the locksmith after you call request_quote. Do not invent prices or ETAs.

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• list_pools

  Surf Session Finder

  List all supported surf pools worldwide with their IDs, names, and locations. Always call this first to get valid pool_id values for the other tools.

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• get_facility_detail

  GTH Intelligence - Substance Abuse Treatment Finder

  Get the full profile of one specific treatment facility: address, phone, programs offered, insurance plans accepted, SAMHSA verification status, and a direct browse URL. Supports partial name matching — returns the best match if multiple facilities contain the query string. Use after search_facilities when the user wants to drill into a named facility.

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• list_states

  GTH Intelligence - Substance Abuse Treatment Finder

  List every US state that has treatment facility data in this directory, with per-state facility counts. Returns an array of {state, stateAbbr, count}. Use as a first step when the user's location is unclear, or to discover where coverage exists before calling search_facilities.

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• get_treatment_types

  GTH Intelligence - Substance Abuse Treatment Finder

  Get definitions of all treatment program types (Inpatient Rehab, Detox, PHP, IOP, Outpatient, MAT, Counseling, Sober Living) with duration, intensity, and typical fit. Returns markdown-formatted explanations. Use when the user is uncertain which treatment_type to pass to search_facilities, or asks 'what's the difference between X and Y'.

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• ask_pipeworx_grounded

  Tinder Bio

  Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,358 across 1395 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

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