"A search assistant for finding Hisense air conditioners" matching MCP tools:

• content_discoveryA

  gapup-mcpC

  Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.

  ConnectorNo auth

• content_discoveryA

  mcp-knowledgeC

  Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.

  ConnectorNo auth

• content_discoveryA

  gapup-mcpC

  Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.

  ConnectorNo auth

• weather.forecastA

  Abstraxn Agent LayerA

  Weather data from a global weather data provider — current conditions, short-range forecast, astronomy, historical, marine, and location search. Free, read-only, no wallet needed. Every action requires `q`, a location: a city name (e.g. "Paris"), "lat,lon" (e.g. "48.85,2.35"), a US/UK/Canadian postal code, or an IATA airport code. Pick one `action`: - current: current conditions for `q`. Optional `aqi` (default false) adds basic air quality data. - forecast: forecast for `q`. Optional `days` — this deployment runs on a data plan capped at 3 days, so only 1-3 is accepted; omit for the provider default. Optional `aqi`, `alerts` (default false, adds active weather alerts for the location where available). - astronomy: sunrise/sunset/moonrise/moonset/moon phase for `q` on `date` (required, YYYY-MM-DD). - search: resolve a fuzzy `q` into matching locations (name, region, country, lat/lon) — use this first if you only have an approximate place name and need to disambiguate. - history: past weather for `q` on `date` (required, YYYY-MM-DD, must be yesterday). This deployment enforces a free-tier data plan that only allows yesterday's date — any other date is rejected before a provider call is made. - marine: marine/sailing conditions for `q`. This deployment runs on a data plan limited to 1 day of data with no tide information. Results are for general informational purposes only — do not use them as the sole basis for decisions involving personal safety, aviation, marine navigation, or emergency planning.

  ConnectorNo auth

• weather.forecastA

  AbstraxnA

  Weather data from a global weather data provider — current conditions, short-range forecast, astronomy, historical, marine, and location search. Free, read-only, no wallet needed. Every action requires `q`, a location: a city name (e.g. "Paris"), "lat,lon" (e.g. "48.85,2.35"), a US/UK/Canadian postal code, or an IATA airport code. Pick one `action`: - current: current conditions for `q`. Optional `aqi` (default false) adds basic air quality data. - forecast: forecast for `q`. Optional `days` — this deployment runs on a data plan capped at 3 days, so only 1-3 is accepted; omit for the provider default. Optional `aqi`, `alerts` (default false, adds active weather alerts for the location where available). - astronomy: sunrise/sunset/moonrise/moonset/moon phase for `q` on `date` (required, YYYY-MM-DD). - search: resolve a fuzzy `q` into matching locations (name, region, country, lat/lon) — use this first if you only have an approximate place name and need to disambiguate. - history: past weather for `q` on `date` (required, YYYY-MM-DD, must be yesterday). This deployment enforces a free-tier data plan that only allows yesterday's date — any other date is rejected before a provider call is made. - marine: marine/sailing conditions for `q`. This deployment runs on a data plan limited to 1 day of data with no tide information. Results are for general informational purposes only — do not use them as the sole basis for decisions involving personal safety, aviation, marine navigation, or emergency planning.

  ConnectorNo auth

• lookup_provider_taxonomyA

  mcpB

  Look up a NUCC provider taxonomy code (the specialty codes used on HIPAA transactions + NPI registrations) or search by keyword. Returns classification + type + optional specialization. Source: NUCC Health Care Provider Taxonomy Code Set (public; CMS-accepted). WHEN TO USE: Resolving a NUCC provider taxonomy code to its specialty description. WHEN NOT: For finding a provider by specialty in a region (not yet implemented).

  ConnectorNo auth

• explain_findingA

  MalinoisA

  Returns the plain-language meaning and step-by-step fix for one Malinois finding. Use it while helping the user fix an issue reported by scan_app, or when they ask what a finding means. Pass the rule_id exactly as scan_app returned it. Read-only, no network, instant.

  ConnectorNo auth

• record_finding_verdictA

  aictrlA

  Apply a post-hoc triage verdict to a finding that was previously recorded via record_finding. Used by the /reply-to-code-review flow to persist whether a finding is TRUE / FALSE / UNCERTAIN and the FIX / DEFER / IGNORE action taken. The finding is located only by the exact `finding_id` returned by list_pr_findings, on ANY lifecycle status (open, fixed, or dismissed — so verdicts still apply after a PR is merged and findings were swept to dismissed). Only findings recorded into the data layer (the aictrl-dev MCP path) can receive a verdict. Idempotent: re-applying overwrites the prior verdict. A not_found response is an application error; call list_pr_findings to refresh stale IDs and do not treat the write as successful. `judged_at_sha` is required: send the full 40-hex commit SHA of the code you actually read. A SHA that is neither the review SHA nor a later commit of the same PR is rejected with validation_error. `reasoning` is required too (#5364): a verdict whose reasoning cannot be audited later is not worth storing. Each write appends to the finding's judgment history; a re-judge from the same run at the same commit updates that run's own row rather than appending a duplicate. A FALSE verdict on an OPEN finding of severity MINOR or NIT (medium/low/info) also dismisses it as a false positive, with your `reason` as the note; on MAJOR or BLOCKER (high/critical) it stays a suggestion a person confirms. The result reports `autoDismissed` (and `autoDismissReason` when false). A dismissal is not reopened by this tool; a person can restore the finding from the pull-request page.

  Connector

  Destructive

  API key

• search_opinionsA

  Court ListenerA

  Search US court opinions from CourtListener — 8.3 million of them, with no API key and no rate limit. Covers the UNITED STATES SUPREME COURT (SCOTUS), the federal circuit courts of appeals, the federal district courts, and the state courts (71% of the corpus is state case law). THE tool for what a US court HELD, decided or ruled in a NAMED CASE — "What did the Supreme Court hold in Air France v. Saks?", "Miranda v. Arizona", "the Ninth Circuit ruling in ..." — including the treaty and statutory questions such cases turn on (Warsaw Convention, ERISA, the Fourth Amendment). Matches the case name, the judge, and (where CourtListener recorded one) a nature-of-suit/disposition category, so it is strongest on party names, judges, and case categories ("workers compensation", "habeas", "immigration") and weaker on a legal DOCTRINE that would not appear in those fields (e.g. "qualified immunity") — use find_case when you know the party, and expect this to sometimes miss on doctrine-only phrasing. Filter by jurisdiction (state vs federal, inferred from the citation reporter) and by date_filed (the real ruling date). A specific court code (e.g. "scotus", "ca9") or a filing after the covered window falls back to a live, token-less CourtListener search automatically. Every result carries snapshot_date and opinion_ids to read with get_opinion.

  ConnectorNo auth

• show_tours_activitiesA

  GuruWalkA

  Present a selection of tours or activities to the traveler as a visual list of cards (photo, rating, price, booking button) rendered inline in the conversation. Showing cards is the default way to present tours to the traveler: whenever your reply features specific tours (recommendations, a shortlist, availability results), call this tool alongside your text instead of waiting to be asked. Don't re-render a selection you already showed unless it changed. Call it AFTER finding tours with the search tools — it is a presentation tool, not a search tool. If you already checked availability with get_product_availability, you can pass each item's sessions (date and start time) and the cards will highlight them with booking links preselecting the date. For clients without UI support the same data is returned as structured text.

  ConnectorNo auth

• regenerate_scheduleA

  My TV ChannelA

  Rebuild the channel's schedule now using the current weights. Call this after changing weights on a channel that is ALREADY live, otherwise the change will not air until the hourly cron. Throttled to one call per channel per 5 minutes; exceeding that returns 429 with a Retry-After header giving the seconds remaining. Requires the channel to meet launch requirements.

  Connector

  Destructive

  No auth

• validate_schedule_rulesA

  My TV ChannelA

  DRY-RUN a ruleset. Saves nothing. Returns {valid, errors, warnings, preview} where preview is a simulated 24h grid of what would actually air. Intended as the dry run for set_schedule_rules: rules are wall-clock, slots start on-or-after their time, and dayparts reweight the random fill, so the airing result is hard to predict from the rules alone. Unlike set_schedule_rules this is not tier-gated and works on any tier. Same payload as set_schedule_rules.

  ConnectorNo auth

• edgar_full_text_searchA

  US Economic, SEC EDGAR & On-Chain Data (x402)A

  Full-text search across all SEC EDGAR filings since 2001 for a keyword or phrase. Wraps EDGAR's own full-text search index, so it covers every filer and form type, not just a single company. Useful for finding who is disclosing a particular risk, technology, litigation, or event across the entire market. When to use: cross-company research ("who is disclosing AI-related risk factors"), finding filings that mention a specific term, litigation or regulatory tracking. When NOT to use: you already know the company (use edgar_filings_feed, which is company-scoped and cheaper), or you need results from before 2001 (EDGAR full-text search does not cover that far back). Args: - query (string, required): search text. Wrap an exact phrase in double quotes, e.g. "\"material weakness\"". - forms (string[], optional): restrict to form types, e.g. ["10-K"]. - dateFrom (string, optional): ISO start date (YYYY-MM-DD). - dateTo (string, optional): ISO end date (YYYY-MM-DD). - limit (integer, optional, default 10): maximum hits to return (1-50). Returns structuredContent: { "query": "material weakness", "totalMatches": 10000, "totalIsApproximate": true, "count": 2, "hits": [ { "id": "0001193125-26-123456:doc.htm", "entity": "Example Corp.", "form": "10-K", "filedAt": "2026-03-01", "cik": "0000320193" } ], "source": "https://www.sec.gov/edgar" } "totalMatches" is a lower bound and "totalIsApproximate" is true once EDGAR's own count exceeds its display cap (10,000) — narrow with forms/dateFrom/dateTo for a precise count.

  ConnectorNo auth

• get_facility_staff_rosterA

  Georgia Prisoners' Speak Public DataA

  Staff headcount and list for one GDC facility, from a GDC-produced roster. Answers "how many staff work at X", "how many officers work there", "list the staff at X". `slug` is the facility slug, e.g. 'dooly-state-prison' (use list_facilities to find it). `title` optionally filters by job title. GDC abbreviates heavily: correctional officers appear as "Correctional Ofc", clerical staff as "Admin Support" or "Admin Assistant". Try both the plain word and the abbreviation. ALWAYS report the `observed` date alongside any figure from this tool. A roster is a single-day observation, not a statement about who works there today. A zero for a role is a finding about the roster rather than missing data: the roster holds no nursing, physician or dental titles anywhere in GDC, which is consistent with medical staffing being contracted rather than directly employed. Do not assert that reason as established fact.

  ConnectorNo auth

• tiktok_search_videosA

  Manifold MCPA

  TikTok videos matching a keyword, ranked by the platform's own search. Use when: "what is TikTok saying about X", finding creators in a category, watching a product or a brand name. Not for: one account's own videos, use tiktok_get_videos. A complete window of everything posted (TikTok search is ranked and never complete). Returns: List, ranked. rows[]: videos with author and author_followers, created_at, text (cut at 2000 characters), likes, comments, shares, saves, views, media, duration_s, links, transcript and top_comments[]. saves is how many people saved it, music the sound, and author_followers sizes each creator without another call. null or empty = the platform does not publish it. Cost: 1 credit per page. Cached 6 hours. Example: {"query": "creatine gummies", "since": "month", "sort": "popular"}

  ConnectorAPI key

• search_placesA

  China FootnotesA

  Search China Footnotes for sights and neighbourhoods in the Chinese cities it covers. Matches English or Chinese names, aliases, district, type and tags. Suited to questions about what to see or do in a city, and to finding a place's id from its name. With no arguments it lists places and the city ids covered; asked about a city it does not cover, it says so rather than returning unrelated places. Returns short summaries with ids and page links, best match first; matchedAll is false when no place matched the whole query and the results only share some words with it. Opening hours, tickets, booking and traveler tips come from get_place.

  ConnectorNo auth

• amazon_get_keyword_metricsA

  Manifold MCPA

  Monthly Amazon search volume for up to 1,000 keywords you already have, in the order given. Use when: "how many people search Amazon for X", comparing Amazon demand with Google demand, sizing a product idea on Amazon. Not for: Google volume, use seo_get_keyword_metrics. Finding keywords, use amazon_search_keywords. Returns: List. rows[]: keyword, volume (monthly Amazon searches; null = no data). Cost: 5 + 5 per 100 keywords. 10 keywords = 6 credits. Cached 7 days. Example: {"keywords": ["standing desk", "desk converter"]}

  ConnectorAPI key

• list_findingsA

  EnokiA

  List security/safety findings for your workspace, most recent first. Optionally filter by status, target model, minimum severity, or a specific run. Returns a compact summary; call get_finding for the repro detail of any one finding. On a plan that withholds a finding's proof, category reads '[redacted]' and get_finding returns the same — the finding and its severity are real, the evidence is withheld. Paginated: 200 findings per page (max 200). The result carries total_matched and, when there is more, next_cursor: pass it back as 'cursor' with the same filters (it is bound to them) to read the next page, until it is absent.

  ConnectorOAuth

• find_similar_elsewhereA

  Cubi EstateA

  Homes like one listing, in another city, side by side with it. Use when the user likes a home and asks what something similar costs, or looks like, elsewhere ("homes like #2 in Lisbon"). Returns the listing and up to 2 matches in `city`, each with asking price, price per m², stated features, environment readings (quiet, clean air, leafy, coastal, flood / wildfire / seismic / ground-motion screening), walk and drive minutes, and the region's climate. Matches have the same bedrooms and a living area within 15%, and are picked on features and environment — never on price — so the price difference is a finding, not how they were chosen. Relay it as asking prices, never as a saving, a valuation or a "city premium". A feature marked "not_stated" means the listing doesn't say, not that the home lacks it; an environment value "unknown" means there is no reading. When `matches` is empty, say no documented match was found; do not loosen it. With `compare_with` (a second listing id) instead of `city`, no search runs: the two listings come back side by side on the same rows — e.g. the top home of each city in a comparison, feature by feature. Args: listing_id: Source-prefixed id of a listing already shown (e.g. `remax:12345`). Do not invent one. city: The other city to search in, e.g. "Lisboa". compare_with: A second listing id already shown, to compare the two. lang: ISO 639-1 code (en/pt/es/fr/de/nl/ru). Returns: JSON: seed and matches[] with price, eur_per_m2, features, environment {axis: {state, value, unit}}, minutes, climate, and a `card` with the listing link; or a not-found / same-city message.

  ConnectorNo auth

• dante.remediate_hallucinationA

  Dante IX — Brand & GEO IntelligenceA

  Synthesize structured semantic counterweight artifacts to correct an inaccurate or outdated claim (hallucination) made by an AI assistant (ChatGPT, Perplexity, or Google Gemini) about a brand. Returns a JSON package (`markdown_counterweight_block`, `html_claim_review_snippet`, `faq_json_ld_script`, `verification_prompts`, and `domain_seal_sha256`). Side effects & latency: Purely deterministic, read-only synthesis (<300ms) with zero external LLM calls and zero destructive behavior. Security & parameter constraints: `brand_name` must match the stem of `domain_url`, and `verified_fact` must not contain third-party foreign URLs. Auth & quota: Consumes 0 live audit credits. Runs in 30% preview mode at $0 cost when `DANTE_API_KEY` is omitted; returns 100% unredacted `ClaimReview` and `FAQPage` artifacts when called with a paid `dnt_live_` license key. When to use: Use `dante.remediate_hallucination` when an AI assistant is actively stating false pricing, wrong features, or outdated ownership about a brand and you need an authoritative `Schema.org ClaimReview` + `FAQPage` correction block. Use `dante.forge_geo_shield` instead for general site-wide `/llms.txt` and `Organization` schema generation, or `dante.probe_geo_share` to check current AI citations.

  ConnectorNo auth

• international_quoteA

  warp-agent-mcpA

  Air, ocean and US / Mexico cross-border truck rates for a shipment with at least one end outside the US / Canada (import, export, or between two other countries). Describe the cargo ONE way: items (pallets / pieces with dimensions), containers (ocean full container load: 20'DV, 40'DV, 40'HC), or volume (total cubic feet when piece sizes are unknown). Returns every option: mode (AIR / OCEAN / TRUCK), service (LCL, FCL, STANDARD, EXPRESS, ...), door service (door_to_door, door_to_port, port_to_door), price, transit days, ports and how long the rate is valid. door_to_port and port_to_door prices do NOT include charges at the far port; tell the user that plainly, and the user must accept those charges before booking. Not served: hazardous materials, sanctioned countries, and pickup and delivery in the same non-US country. Pricing runs live and can take up to a minute; if it times out, retry once. For domestic US freight use compare_modes or ltl_quote instead. Book the user's chosen option with international_book. Requires a signed-in Warp account.

  ConnectorNo auth