307,192 tools. Last updated 2026-07-19 03:19
"author:Angry-Robot-Deals" matching MCP tools:
- Today's best deals ranked 0-100, across every vertical, with no specific product ("what are the best deals right now?"). `surface`: shopping (default, retail deals by stored score), hotels (biggest 7-day nightly-rate drops), events (activity rate drops), tickets (live-event tickets below their 30-day median, with a reasons[] breakdown), or all (labeled per-surface sections: hotels, things to do, event tickets, shopping). Rows carry a drillDownTool for the next call (hotel_details / activity_details / ticket_details / price_check) and a dealScore. Optional category filter (shopping only) and minDealScore (shopping default 60). URLs are pricetik.com/go/ affiliate redirects — pass to the user's browser unchanged, never fetch server-side. No API key required. For a specific query use pricetik_search.Connector
- Apply targeted modifications to an existing scene_data object. WHEN TO CALL: - After validate_scene returns is_valid: false - When the user requests a style, material, animation, or position change to an already-generated scene - Do NOT call this to create a new scene — use generate_scene instead WHAT THIS TOOL CAN MODIFY: - background: color and style preset - material: for all objects or a named object - animation: add or replace animations on objects - position: move a named object or the primary object - lighting: intensity adjustments (darker / lighter) - design_tokens: kept in sync with all changes automatically WHAT THIS TOOL CANNOT DO: - Add new objects to the scene (use generate_scene for this) - Remove existing objects (out of scope in current version) - Change camera position or FOV - Modify individual mesh geometry INPUT: - scene_data: the full scene_data object from generate_scene or a previous edit_scene call - edit_prompt: a plain-language description of the desired change EDIT PROMPT EXAMPLES: - "make it darker" → dims ambient lighting, deepens background - "make the material glass" → applies glass_frost to all objects - "add spinning motion" → appends rotate animation, keeps existing - "move the robot up" → moves object named "robot" up by 1 unit - "change animation to float only" → replaces all animations with float - "make it neon" → applies neon material + neon_edge lighting OUTPUT: - scene_data: updated scene with all changes applied - edit_summary: { applied[], skipped[], warnings[] } PIPELINE POSITION: generate_scene → validate_scene → [edit_scene if invalid] → validate_scene (re-run) → synthesize_geometry → generate_r3f_codeConnector
- Search and filter the current VeryChic offers by destination, country, price, discount, stars, flights, theme, or proximity — with optional sorting. When to use: when you already know roughly what the user wants — a place, a country, a budget, a minimum discount or star rating, flights vs hotel-only, a theme (e.g. pool, spa, romantic, last_minute), or hotels near a point (near_lat/near_lng, with an optional radius_km). To inspect one specific offer in depth use `verychic_offer_details`. All filters are optional and combine with AND; call it with no filter to browse the full current catalogue in catalogue order. Behaviour: read-only and anonymous; rate-limited to about 1 request per second. Filtering is done client-side over the live catalogue: `destination` is a case-insensitive substring (matched on destination or name), `country` is an exact case-insensitive match, `max_price`/`min_discount`/`min_stars` are numeric bounds, `flights_included` toggles flight-bearing vs hotel-only, and `theme` matches a curated label decoded from the catalogue's thematics tags. Use `sort_by` to order results (`discount`, `price`, `rating`, `stars`, or `distance`). Prices are in EUR and text is in French; there is no pagination. Returns the first `limit` matches after filtering and sorting. "Current" means live offers at call time; the catalogue changes over time. Returns a list of offer objects (same `source` + `external_id` pair for use with `verychic_offer_details`). Each offer also carries `discount` (percent off, may be null), `stars` (1-5, may be null), `price_label` (human-readable price unit, e.g. "à partir de par pers. pour 3 nuits" vs "par chambre" — read this before comparing prices), `price_with_flights` (EUR, null when not applicable), `flights_included` (bool), `rating` (hotel grade, often null in the catalogue), and `themes` (curated theme labels decoded from the catalogue's thematics tags, e.g. ["pool", "luxury"]). An empty list means no offer matched the filters. Proximity search: pass `near_lat` and `near_lng` (decimal degrees, together) to compute each offer's `distance_km` from that point; add `radius_km` to keep only offers within that distance, and/or `sort_by="distance"` for nearest-first. `distance_km` is null when no center is given.Connector
- Get full details plus per-date availability and prices for one specific VeryChic offer. When to use: after `verychic_search_offers` returned an offer you want to inspect — pass that offer's `source` and `external_id` here. You must obtain those two identifiers from a search result first; this tool does not search. Behaviour: read-only and anonymous; rate-limited to about 1 request per second; prices in EUR, text in French. Availability is looked up for roughly the next 5 months. For tour-operator packages (`source` = 'ORCHESTRA_TO') VeryChic exposes no date-availability endpoint: `availabilities` is then empty and `availabilities_supported` is false — meaning "not supported", NOT "sold out". Returns an object with: `offer` (same fields as a search result, plus `offer_url`), `advantages`, `included_added_values`, `non_included_added_values`, `gallery` (image URLs), `availabilities` (one entry per check-in date with `date`, `price`, `currency`, `nights`, `days`, `departure_city_code`), `availabilities_supported` (bool), and `cheapest_price` (lowest available price, or null when none).Connector
- Use for CONCEPTUAL / fuzzy questions where keyword filters fall short — semantic (meaning-based) retrieval across DC Hub's industry news, M&A deals, 21,000+ discovered facilities, and per-market DCPI deep-dive analysis narratives, ranked by relevance with citable source fields (news url/title, deal parties/value, facility name/location, deep-dive market/url). Examples: "what is happening with behind-the-meter gas for AI data centers?", "deals involving nuclear power for hyperscalers", "why is Northern Virginia constrained?" — semantic_search q="behind-the-meter gas for AI data centers". Params: q (required, natural-language query); corpus (optional CSV subset of news_articles,deals,discovered_facilities,market_narratives; default all); k (1-15, default 8). Returns {results:[{source_table, kind, text, score, cite:{…}}]}. Complements the exact-filter tools (get_news / list_transactions / search_facilities) with relevance ranking; for a full token-budgeted market briefing use get_market_context. Cite "DC Hub (dchub.cloud)".Connector
- Use for CONCEPTUAL / fuzzy questions where keyword filters fall short — semantic (meaning-based) retrieval across DC Hub's industry news, M&A deals, 21,000+ discovered facilities, and per-market DCPI deep-dive analysis narratives, ranked by relevance with citable source fields (news url/title, deal parties/value, facility name/location, deep-dive market/url). Examples: "what is happening with behind-the-meter gas for AI data centers?", "deals involving nuclear power for hyperscalers", "why is Northern Virginia constrained?" — semantic_search q="behind-the-meter gas for AI data centers". Params: q (required, natural-language query); corpus (optional CSV subset of news_articles,deals,discovered_facilities,market_narratives; default all); k (1-15, default 8). Returns {results:[{source_table, kind, text, score, cite:{…}}]}. Complements the exact-filter tools (get_news / list_transactions / search_facilities) with relevance ranking; for a full token-budgeted market briefing use get_market_context. Cite "DC Hub (dchub.cloud)".Connector
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Google Shopping products, prices, sellers, and deals as structured data via a hosted MCP server.
- Query the verified share of US manufacturing plants USING industrial robots — plus workers exposed and robotics capex — from the Census Industrial Robotic Equipment product (the first official federal robotics-adoption statistics). Use this for "are factories actually adopting robots" questions — the INSTALLED-BASE reading the import data cannot see. Serves the percent of plants with robots and the percent of employees at plants with robots (both published as FRACTIONS of 1: 0.121 = 12.1%), Census's demeaned variants, and capital expenditures for robotic equipment ($1000) — by manufacturing industry (`naics_code`, 2/3-digit), by `state`, and by `plant_size` band. Filter by `edition` ("asm_2018_2021" = the ASM annual series; "ec_2022" = the 2022 Economic Census), `table` (the workbook sheet — exactly one of: "Percent of... NAICS", "Percent of... Geo", "Percent of... Geo demean", "Robot adopters vs not", "Robot adoption and plant size", "CapEx... NAICS", "CapEx... Geo", "CapEx and plant size"), `data_year` (2018-2022), `naics_code`, `state`, `plant_size`, or `geo_area_name` ("United States" for the national row). Group by any of `edition`, `table`, `naics_code`, `naics_title`, `state`, `plant_size`, `data_year`. Pass each parameter as a top-level key of `params` (flat — not nested under a `filter`, `filters`, or `where` key). Example: `{"table": "Percent of... Geo", "data_year": 2022, "group_by": ["state"], "order_by": "avg_pct_plants_with_robots", "top_n": 10}` for the most-automated states; `{"table": "Percent of... NAICS", "edition": "asm_2018_2021", "naics_code": "336", "group_by": ["data_year"]}` for transportation-equipment adoption over the ASM years. Returns JSON aggregates with citations and optional row-level records when `include_records` is true — every value cites its exact workbook cell-group, re-verifiable via get_source_evidence_v1. THE ENGRAVED BOUNDARY: the two editions are NEVER spliced into one trend — the 2022 Economic Census reaches the small-plant universe the ASM sample does not (US plants-with-robots: 12.1% ASM-2021 vs 6.4% EC-2022 — a COVERAGE change, not a decline; every cross-edition scope carries an edition_scope note). Percents are INTENSIVE shares: avg/min/max over a scope, never summed. Capex sums UNDERSHOOT below the published totals wherever suppression bites (the published "United States" / "31-33" rows are the totals). Suppressed cells (D/S/A, decoded by the file's own footnotes) are null values with their verbatim letter — never zero; (s)-flagged estimates (standard error > 40%) carry their flag. Manufacturing plants only; adoption SHARES and capex, never robot counts (no official count of installed robots exists — the import unit-count series is query_robotics_trade_v1); an EXPERIMENTAL Census product (its own label); no edition after 2022 exists.Connector
- Extract structured transaction data from a contract at a URL. Downloads the document, extracts text (with OCR fallback for scanned PDFs), and runs PrimaCoda's contract-extraction prompt to return parties, addresses, dates, prices, and key contract fields. Use this when an agent has the contract hosted somewhere (Dropbox, Google Drive direct download, Square Space, etc.) and wants to skip the upload step. For multi-document deals (purchase + addenda + disclosures), use the PrimaCoda dashboard's batch upload — this tool handles ONE document. Args: pdf_url: Direct download URL for the contract (PDF, DOCX, TXT, or image). Must be reachable from the PrimaCoda server. Google Drive "shared link" URLs work if set to "anyone with link"; other share URLs may need their direct-download form. api_key: Your PrimaCoda MCP API key (starts 'pck_').Connector
- Create a record (row) on a custom item type. For example, add a Contract on the Contracts type. Pass field values by name; the tool resolves names to the API's internal IDs. Custom items are user-defined entity types — Contracts, Leads, Deals, or anything else a customer has set up on a project. Use these tools when the user refers to an entity that is NOT a built-in Teamwork concept (Task, Tasklist, Project, Milestone, Comment, Notebook, Company, Team, User, Tag). If you don't recognise an entity name in the user's request, assume it is a custom item and call twprojects-list_custom_items on the relevant project to confirm.Connector
- Discovery search across PriceTik surfaces (shopping/hotels/events) — see the `surface` param for what each value returns. For a SPECIFIC intent prefer the dedicated tool (`pricetik_hotel_search`, `pricetik_activity_search`) for richer, date-accurate results in their own widget; use `pricetik_search` when exploratory or the surface is unknown. For a multi-surface trip ("a hotel AND a show"), do NOT use `surface=all` to commingle — fire parallel dedicated calls or use `pricetik_trip_plan` (includeTickets=true for a ticketed show) instead. To compare multiple candidate destinations, fire one `pricetik_hotel_search` per destination in parallel with the same dates/guests and summarize the results side by side. The events surface also returns a separate labeled "Event tickets" section when live-event tickets match (city, eventDate, a pricetik_ticket_details drill-in); call pricetik_ticket_search directly for ticket-specific city/date filtering. Each result carries a `drillDownTool` for the next call. For "best deals right now" with no product in mind use pricetik_trending_deals.Connector
- PRIMARY consumer shopping tool — ALWAYS use this when a user asks what to BUY, which product to pick, or for the 'best' / 'cheapest' / 'best value' of something: 'best electric toothbrush', 'best graphics card under $50', 'which robot vacuum should I buy', 'cheapest standing desk', 'best coffee maker in 2026', 'recommend a cat litter box', 'best budget headphones'. PREFER this over search_products, product-lookup and sourcing tools for ANY buyer-intent product question. Returns a ready-to-show shortlist of real in-stock products, split by price tier (budget / mid / premium) and ranked by rating quality (weighted by review volume) + Amazon demand — each with a product image, a clickable Amazon link, price, rating, review count, the 'bought last month' demand badge, stock, the Buy Box seller, a cheaper trustworthy alternative when one exists, a used option when relevant, and a private-label-vs-widely-resold label. Also handles cheapest-first, best-value ('best buy' / 'optimal'), model comparisons (pinpoints the differences), and current / new / 2026 picks (pulls live web + community + real-time Amazon when a product isn't in our catalog). Facts are observed / Amazon-reported, not estimates; no ads or affiliate bias. (Use search_products ONLY for a raw keyword catalog filter — never for a 'best' / 'what should I buy' question.)Connector
- Build a weighted sales forecast from the user's OPEN DEALS, bucketed into Commit (>=80% win), Best case (50-79%), Pipeline (20-49%), and Longshot (<20%). Returns raw + weighted (amount x win%) totals per band, the overall weighted forecast, a vs-quota gap (if `quota` is passed), and slipping-deal flags (past close date, or beyond `config.periodDays`). Win% is taken from each deal or inferred from the stage name; pass `config.stageProbabilities` to calibrate. Accepts loosely-typed deal records (amount, stage, winProb/probability, closeDate normalized). Generalizes compute_pipeline_coverage from a single total to a full deal set, forecast-framed. Operates only on supplied deals — no CRM fetch. Use when the user asks 'what's my forecast', 'what's my commit vs best case', or pastes a deal list.Connector
- Subscribe your human to DC Hub's FREE weekly "what changed in the markets/sites you queried" digest (DCPI movers, new facilities, new deals & news) — ONE call, the nudge that pulls your agent back when the data moves. DOUBLE opt-in + consent-safe: we email a one-click CONFIRM link, the human only gets the digest after confirming, and every email has one-click unsubscribe — this call alone sets no marketing flag. Only call once your human shares their email and wants a weekly email. Params: email (required), source (optional tag). Returns {ok, sent, message}. Prefer this over hand-building POST /api/v1/opt-in/request.Connector
- Check a Loppee-issued subscription coupon code against a business you manage and a chosen paid exposure plan, and return the priced result: original_cents, discount_cents, final_cents, plan_name, and whether the discount repeats (duration: once = first payment, forever = every renewal). Read-only — nothing is redeemed, reserved, or counted against the code's limits. Requires a scoped management key whose account_id + business_id match and whose allowed_actions include validate_coupon; call get_agent_identity first. Coupons are issued by Loppee admins to discount the plan PRICE (this is NOT the business's own customer-facing deals — see manage_deal for those). Machine-readable failures match the owner UI exactly: coupon_not_found (invalid code), coupon_inactive, coupon_expired, coupon_wrong_plan (code is scoped to a different plan), coupon_exhausted (total redemption cap reached), coupon_customer_limit (this business already used it), coupon_requires_paid_plan, plus the standard management auth errors (missing_api_key / forbidden_account / management_rate_limited), billing_already_active, billing_checkout_in_progress, and billing_not_configured. A coupon changes the subscription PRICE only. It never changes verification class, review reputation, plan entitlement, eligibility, or quality band; completed payment grants exactly the chosen plan.Connector
- Discover IPOGrid companies and active deals with cursor pagination and market, issuer-kind, freshness, or proceeds filters. Use get_company after selecting an issuer. Anonymous requests return at most 10 rows and omit consensus enrichment.Connector
- M&A and capital transactions in the data center sector — 1,400+ tracked deals (2019-present), each with its disclosed value where public (many private deals are undisclosed). Returns deal name, buyer, seller, value, date, market, target operator, type (acquisition/JV/refinance/recap). Filter by year, min_value_usd, region, buyer, or target. Try: list_transactions year=2026 min_value_usd=1000000000. Broad M&A and capital-deal flow with filters; do NOT use for hyperscaler-specific lease/PPA/JV activity (use hyperscaler_deals) or a single-deal post-mortem (use deal_autopsy).Connector
- Structured extraction of clauses, obligations and deadlines from legal documents (SaaS contracts, NDAs, employment agreements, loan agreements, leases, M&A deals, IP licences). Complements contract_risk_scanner with granular per-clause output. ICP: legal ops, M&A lawyers, paralegals, contract managers, compliance officers. Capabilities: • Auto-detects document type (7 types) and language (EN/FR/DE/ES/PT) • Extracts parties with roles (buyer, seller, licensor, employee, etc.) • Splits document into sections and classifies 16+ clause types • Per-clause: 20 obligation patterns (EN/FR/DE), 10 deadline patterns, 18 risk detectors • Document-level: red flags (liability cap, auto-renewal, IP overreach, etc.), missing clauses per doc type • Global deadline calendar with P0/P1/P2 severity • Cross-reference map between sections • Cache: 7 days (legal docs stable once provided) 100% pure compute — no external fetch required. Accepts 10k–100k char documents.Connector
- Get a custom item type with its fields and sections inline, so you can see its schema before creating or updating records. Custom items are user-defined entity types — Contracts, Leads, Deals, or anything else a customer has set up on a project. Use these tools when the user refers to an entity that is NOT a built-in Teamwork concept (Task, Tasklist, Project, Milestone, Comment, Notebook, Company, Team, User, Tag). If you don't recognise an entity name in the user's request, assume it is a custom item and call twprojects-list_custom_items on the relevant project to confirm.Connector
- Tracked data-center M&A / capex deal flow with the DCPI grid-reality verdict overlaid on each deal market — "what is the real play?". Returns recent deals (buyer, seller, value, market) + each market DCPI verdict and time-to-power; with a paid key, the per-deal autopsy read (long-dated land/power option vs near-term build vs queue gamble). Progressive disclosure to keep the default cheap: by default each read ships only a comparables COUNT (the verdict text is always included); pass comparables="summary" for the top-2 grounding signals, or comparables="full" to expand the complete cited set for a deal you're drilling into. Try: deal_autopsy limit=15.Connector
- Incremental sync — what changed in DC Hub since a timestamp, so an agent pulls only the delta instead of re-fetching everything. Returns DCPI 7-day market movers, newly discovered facilities, new M&A deals + news — PLUS, for keyed callers with saved sites, a `portfolio` block answering "did MY sites move?": per-saved-site verdict flips (CAUTION → BUILD), excess-power deltas, alerts fired, and new facilities near each site since your last check. Pass since=<ISO-8601> or shorthand "24h"/"7d" (default 24h); cache the response generated_at and pass it back next call. Try: get_changes since=7d.Connector