"A query about Google Chrome or a related Chrome topic" matching MCP tools:

• domain_rankingA

  mcpB

  Get domain popularity rankings from five independent sources: Majestic Million (backlinks), Tranco top 1M (aggregated traffic), Cloudflare Radar (1.1.1.1 DNS query popularity), Cisco Umbrella (OpenDNS query popularity), and Chrome UX Report (real Chrome user traffic).

  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

• search_contentA

  flevyA

  Search Flevy's marketplace of consulting frameworks, PowerPoint templates, Excel financial models, business toolkits, and management case studies. Use this whenever a user needs a best-practice framework, methodology, template, financial model, or real-world case example on any business or management topic (strategy, digital transformation, supply chain, pricing, operational excellence, M&A, etc.). Returns up to 10 relevance-ranked recommendations across two content types: "document" (premium documents authored by management consultants) and "case_study" (management case studies). ALWAYS include each recommended item's url as a clickable link when you mention it in your reply — never reference a document without its link, because the link is the only way the user can open it. Each result carries a content_id for get_content_details. Filters: topic (single, or "topics" for documents covering ALL of several topics), author (list more documents from an author seen in results), filetype (including tier1_consulting_deck for McKinsey-style strategy decks), content_type. Topic-filtered responses also list related_topics to pivot to. Provide at least one of query, topic(s), or author; use list_topics to map user phrasing to a canonical topic.

  ConnectorNo auth

• methodology_faqA

  RefproA

  Answer questions about how Refpro works. Use this when a user asks what Refpro is, what lender-grade means, which deal types are supported, what a deal pack contains, which output formats are available, how the pricing tiers work, or how Refpro compares to spreadsheets and free calculators. Input: query, a free-text question or topic keyword, 200 characters maximum. Returns a 2 to 4 sentence answer, a list of related topic titles, and a canonical source URL on refpro.ai. Backed by a static curated knowledge base, so answers are stable, quotable and identical every time: no LLM-generated text and no network calls. Falls back to a general Refpro overview when the query matches no known topic. Do not use to analyze a deal or compute numbers; call deal_quick_check for that. Do not use for general real estate investing questions; this tool covers Refpro itself.

  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

• get_contextA

  Vilix AIA

  Load the last few recent messages and semantically related past turns. Call this before you compose your reply. Pass the user's latest prompt only. Vilix runs relevance retrieval internally. For anything more specific you may also call the optional search_semantic / search_keyword / recent_messages tools (e.g. when the user names a source like ChatGPT). Every retrieved item carries an ISO timestamp — when two items disagree about a changing fact (a plan, a status, a decision), the NEWEST timestamp is the latest known value. Related turns may include `context_before`/`context_after` (the adjacent turns) and the payload may include `also_related`: additional nearby matches as compact snippets, newest first — check it before concluding a fact is unknown or unchanged. `chat_id` — pass null (or omit) on a brand-new conversation to also receive `user_rules`, `system_behavior`, `active_projects`, and `active_project_state`. Pass the chat_id returned by a prior `save_turn` to skip those — they are already in the chat's own context from turn 1 and re-injecting wastes tokens. `recent_messages` and `related_conversations` are always returned (they're the cross-tool memory bridge). `attachment_context` (optional, default "") — if the user's CURRENT message has an attachment (file, image, code paste, screenshot OCR), pass a short plain-text summary of it here so retrieval can match on the attachment topic in addition to the bare prompt. Pass the SAME summary to `save_turn` for this turn. Empty = no attachment, ignored.

  ConnectorOAuth

• update_wordA

  Lexpad – Vocabulary Notebook & FlashcardsA

  Changes a word's headword, part of speech, level, topic, pronunciation, hard-to-spell mark and spelling note, usage note, corrected form, collocations, expressions, related words or the user's private note. Only the fields given change; meanings and practice progress are not touched. A headword the notebook already has is refused. The previous values are replaced.

  Connector

  Destructive

  OAuth

• searchCollaboratorsA

  qubitsok — Quantum Computing Jobs, Papers & ResearchersA

  Find quantum computing researchers and potential collaborators from 1000+ active profiles. Use when the user asks about specific researchers, who works on a topic, or wants to find collaborators. NOT for jobs (use searchJobs) or papers (use searchPapers). AI-powered: decomposes natural language into structured filters (tag, author, affiliation, domain, focus). Returns profiles with affiliations, domains, publication count, top tags, and recent papers. Data from arXiv papers published in the last 12 months. Max 50 results. Examples: "quantum error correction researchers at Google", "trapped ions", "John Preskill".

  ConnectorNo auth

• scan_discrepanciesA

  ParlayA

  Surface cross-venue price discrepancies between Polymarket, Kalshi, and Limitless as a discovery feed for price discovery and divergence detection. Default threshold is 0.5% spread, below typical round-trip fees — most results are informational, not tradable arbitrage. Raise `min_spread` to 0.03+ for after-fee opportunities. The optional `query` parameter post-filters results by topic keywords on event titles — it does not perform a topic search; for topic-driven retrieval use `discover_markets` or `search_markets`. Pairs with missing volume data on at least one venue are flagged 'volume_unconfirmed'. All results are indicative only — not trade recommendations. Real-money venues only. Orderbook depth is not confirmed in Phase 1.

  ConnectorOAuth

• create_agentA

  WebRun Browser AutomationA

  Create a scheduled agent — a recurring or one-time automation that runs on a timer in a real Chrome browser. Two modes. WORKFLOW MODE: pass workflowId plus a schedule — the platform copies everything else (prompt, starting URL, output contract, model, proxy, policy, files) from the workflow and bakes your variables into the prompt; results and memory stay centralised on the workflow. STANDALONE MODE: omit workflowId and provide name, prompt (a Goal / Ground rules / Stages / Output browsing runbook — see the prompt field description), and a schedule; the connection's environment is used automatically. Ask the user only what it should do and when it should run, in plain language — pick sensible defaults for everything else and state them; never ask about environments or technical settings. Results are delivered to your connected chat and appear in session history.

  ConnectorNo auth

• check_regulatory_changesA

  krabbot-devtoolsA

  Check whether any US federal regulation on a topic is about to bind or about to close for comment. The Federal Register publishes every business day and a single topic search can match over a thousand documents, almost none of which need action — so this scores each one 0-100 on DEADLINE PROXIMITY (effective dates, comment-window closures, document class, and the Federal Register's own E.O. 12866 significance flag) and returns only what is urgent. Use when asked whether a rule affects a business, what compliance deadlines are coming, or to monitor a regulatory topic on a schedule. Filter by topic (free text), agency (Federal Register slug such as food-and-drug-administration), or document type. US federal only; the score ranks urgency, not whether a rule applies to your specific business. Operated by an autonomous AI agent (Krab Bot); the free tier is used here.

  ConnectorNo auth

• crossload_searchA

  CrossloadA

  Search the Crossload catalogue of German Christian content (sermons, books, audio, images) and get matching items with title, author, licence, duration and a teaser. Use it whenever the question is what was preached, written or said about a topic, a bible passage, by an author, or within a length limit ('something good under 30 minutes'). Use 'uids' to narrow a set of items you already found; it returns the items, not the position of a passage inside them. Topic, author and series take names and are resolved server-side; if a name is not an exact match the search is NOT run and the answer says so — call crossload_browse to get the exact spelling. Bible references are given in German ('Epheser 2', 'Joh 3,16-18') and filtered by range, verses included. Note that 'query' is matched semantically, not literally: it always returns something, so a hit is not proof that the words appear in the text. Combine it with a filter when precision matters. Natural-language questions (German question words, '?', six or more words) trigger stronger semantic matching. A bible reference inside the query text triggers a server-side boost on matching content that may skew results — use the 'bibleRef' parameter for deliberate passage filtering instead. Set 'withExcerpts' when you need to quote WHERE something is said: every hit then carries an excerpt around the match, and matching becomes literal, so a hit does prove the words appear and no hit means they do not.

  ConnectorNo auth

• searchA

  AlphAIA

  ChatGPT connector contract: search AlphAI's AI-enriched financial news with a natural-language query. Ticker symbols (NVDA, BTC-USD), company names (nvidia, tesla) and topic words (insider, earnings, ipo, crypto…) in the query are resolved to structured filters; a query that names nothing known, or whose filters match no stored article, returns an empty results list rather than unrelated stories (rephrase with a ticker, company name or topic word). Each result carries an id for the fetch tool. For precise filtered queries prefer alphai_news_search.

  ConnectorAPI key

• cisa_get_alertsA

  cisa-cybersecurity-mcp-serverA

  List what CISA has published recently — its combined advisory feed, its alerts feed, or its ICS advisory feed. Cursors continue within the current 30-item rolling window; upstream provides no pagination, history, or date-range query. The window's coverage varies from about a week to about two months depending on the feed. For ICS advisory history beyond the window, use cisa_search_ics_advisories, which covers the full corpus back to 2010.

  ConnectorNo auth

• propose_descriptionA

  ShearQuery — Barber & Beauty Industry DataA

  Draft a new Google business description (up to 750 characters; 250+ reads best). Google suspends listings over this field, so the draft is refused if it contains a link, phone number, email, prices or offers, HTML, all-caps shouting, or repeated keywords. Write it about what the business actually does, from my_google_profile. Creates a DRAFT only — nothing on Google changes. Show the owner the draft this returns, word for word, and publish it with publish_change only after they say yes.

  ConnectorNo auth

• propose_attributesA

  ShearQuery — Barber & Beauty Industry DataA

  Draft yes/no answers to attributes from my_attribute_options. Each is a factual claim about the business, so only draft answers the owner has confirmed in this conversation. Creates a DRAFT only — nothing on Google changes. Show the owner the draft this returns, word for word, and publish it with publish_change only after they say yes.

  ConnectorNo auth

• list_contactsA

  AccountHubA

  Browse ALL of the user's SAVED Google Contacts for one account, one page at a time — unlike search_contacts, no query is needed, so this is how to see the whole address book (or find someone when a name search comes up empty/ambiguous). Returns each contact's name, primary email, full email list, and a `next_page_token`: pass that back as `page_token` to fetch the next page, and stop once it's null. Only ONE account per call (pagination is per-account) — pass `account` when more than one Contacts-enabled Google account is connected (call list_accounts to learn labels); it auto-resolves when only one is connected.

  ConnectorOAuth

• console_get_search_analyticsA

  Manifold MCPA

  Clicks, impressions, CTR and average position from the user's own Search Console or Bing data, grouped by query, page, country, device or date. Use when: "which queries bring traffic to my site", "how did page X do last month", "impressions by country", finding queries that rank on page two, a rank check on the user's own site. Not for: a site the user does not own, use seo_get_ranked_keywords. Search volume or difficulty, use seo_get_keyword_metrics. Why a page is not indexed, use console_inspect_url. Returns: List, most clicks first, ties broken by impressions descending. rows[]: query, page, country (ISO alpha-3), device, date (the dimensions asked for; null otherwise), clicks, impressions, ctr (0 to 1), position (average, 1-based). Google data lags about two days and keeps 16 months; Bing query and page rows are the last weeks with no date range, its date rows are daily totals. Totals (sum of clicks, impressions) differ by grouping due to Google's aggregation; this is normal Google behaviour. Cost: 0 credits. Cached 6 hours. Example: {"property": "sc-domain:example.com", "dimensions": ["query", "page"], "start_date": "2026-08-01", "end_date": "2026-08-31"}

  ConnectorAPI key

• reddit_search_postsA

  Manifold MCPA

  Reddit posts matching a query, ranked by the search, across all of Reddit or inside one community. Use when: "what do people on Reddit say about X", "find threads complaining about Y", "who is asking for a tool like ours". Not for: everything new in a community since you last looked, use reddit_get_new_posts (ranked search is never complete). Reading one thread, use reddit_get_post and reddit_get_comments. Choosing communities to watch, use reddit_search_subreddits. Returns: List, ranked, not exhaustive. rows[]: id, subreddit, title, author, created_at, score, comments, upvote_ratio, url, link_url, body (cut at 2000 characters), flair, over_18, is_video. Cost: 1 credit per call, up to about 25 posts; nothing on NoData. Cached 1 hour. Example: {"query": "hubspot alternative", "sort": "new", "time_range": "month"}

  ConnectorAPI key

• reddit_search_subredditsA

  Manifold MCPA

  The communities that discuss a topic, counted from one page of search results. Use when: "which subreddits talk about X", picking the communities to pass to reddit_get_new_posts, sizing where a conversation happens. Not for: a full directory of subreddits about a topic (this counts a sample of posts, so a quiet community can be missing). One community's rules and size, use reddit_get_subreddit. The posts themselves, use reddit_search_posts. Returns: List, most posts in the sample first. rows[]: name, url, posts_in_sample, example_post_url. Search carries no community size; reddit_get_subreddit gives weekly visitors. Cost: 1 credit. Cached 1 hour. Example: {"query": "cold email deliverability", "time_range": "year"}

  ConnectorAPI key