"Google search or related query" matching MCP tools:

• perspective_list

  Perspective AI

  Lists perspectives — either browsing one workspace or searching by title across every workspace the user can access. Items include perspective_id, title, status, conversation count, and workspace info. Behavior: - Read-only. - Browse mode (workspace_id, no query): lists every perspective in that workspace. - Search mode (query): matches against the perspective title across accessible workspaces. Optional workspace_id narrows the search. Query must be non-empty and ≤200 chars. - Errors with "Please provide workspace_id to list perspectives or query to search." if neither is given. - Pass nextCursor back as cursor; has_more indicates further results. When to use this tool: - Resolving a perspective_id from a name the user mentioned (search mode). - Browsing a workspace's perspectives to pick or summarize. When NOT to use this tool: - Inspecting one known perspective in detail — use perspective_get. - Aggregate counts or rates — use perspective_get_stats. - Fetching conversation data — use perspective_list_conversations or perspective_get_conversations. Examples: - List all in a workspace: `{ workspace_id: "ws_..." }` - Search by name across all workspaces: `{ query: "welcome" }` - Search within a workspace: `{ query: "welcome", workspace_id: "ws_..." }`

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• search

  Sofya

  Search the web for current information on any topic. Returns extracted page content, not just snippets. Best for factual lookups, specific questions, or when you need a list of sources. For open-ended questions that need synthesis across many sources, use the research tool instead. For news queries (current events, breaking news, politics, world events), set topic="news" to search news sources specifically. This returns recent articles with publication dates. Set include_answer=true to get an AI-synthesized answer alongside results (adds 5 credits). This is the sweet spot for most agent tasks, e.g. basic + include_answer = 8 credits, much cheaper than a full 25-credit research call. Returns: query, answer (if requested), results (array of {title, url, content, description, fetched, published_date}), search_depth, topic, elapsed_ms, credits_used, credits_remaining, altered_query. Args: query: The search query search_depth: "basic" (default) for extracted page content (3 credits), "snippets" for SERP snippets only without page fetching (1 credit) max_results: Number of results (default 10, max 20) include_answer: Generate an AI answer that synthesizes the search results (adds 5 credits) include_domains: Only include results from these domains (max 10) exclude_domains: Exclude results from these domains (max 10) topic: "general" for web search, "news" for news articles. use "news" for current events, breaking news, politics, or any time-sensitive query freshness: Filter by recency - "day", "week", "month", "year", or "YYYY-MM-DD:YYYY-MM-DD"

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• search

  Melvea Local Honey Discovery

  Search the Melvea local honey directory by free-text query and return matching producers as a list of results (id, title, url). Designed for ChatGPT Deep Research and Company Knowledge. Use for any local-honey discovery query that names or implies a place; the tool parses place and varietal from the query. Returns an honest empty list when nothing matches — never fabricate. Pair with fetch to retrieve full producer detail.

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• general_search

  Nansen

  General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)

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• search_contacts

  xmagnet

  Search for contacts by title, company, or query. Searches saved Xmagnet contacts first (free, instant), then a profile-first prospecting page of up to 50 profiles (free, emails HIDDEN). Examples: 'CTOs in Denver', 'John Smith at Google', 'VPs of Sales at SaaS startups'. Emails are not included — to reveal one, call find_email for that person (4 credits per verified find). Use load_more_contacts for the next page.

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• google-trends.categories

  Vee3

  List all Google Trends category and subcategory labels you can pass to other Google Trends tools in the category field. Returns cat (array of category names, including All categories) and msg. Use this before interest-over-time or interest-by-region calls when filtering by category. Cost = 5 tokens.

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• google-search.url_traffic_insights

  Vee3

  Get Google keyword traffic insights and related keyword suggestions for a URL. Returns an array of keyword suggestions. Each item includes text, monthly search volume, competition_level, competition_index, low_bid, high_bid, and trend. Required: url and language (for example en). Optional: location (for example US) for country-specific data; omit location for global results (default). Optional: min_search_volume (default 0) and intent (informational, navigational, commercial, or transactional). Cost = 20 tokens.

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• search_patents

  patents-trademarks-ip-mcp-server

  Search worldwide patents by keyword, inventor, assignee, or phrase using Google Patents. Returns patent id, title, assignee, inventor, filing/publication dates, and a snippet. Args: query: Free-text query (e.g. "quantum error correction", "lithium battery anode"). max_results: Maximum number of patents to return (1-30, default 10).

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• general_search

  Nansen

  General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)

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• search_notes

  anamnese

  Search notes by keyword or list recent notes. Returns summaries (id + description) only. Use get_note to retrieve the full content of a specific note. With query: Case-insensitive keyword search on description and content. Without query: Returns most recently updated notes.

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• search_google_scholar

  Academic Research MCP Server

  Query Google Scholar for academic papers, citations, and research articles across all disciplines. Returns paper title, authors, publication venue, citation count, abstract preview, and full-text link if available. Use for comprehensive literature searches, citation tracking, or finding highly-cited works.

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• maps_place_details

  Google_maps

  "Hours / phone / reviews of [business]" / "Google business info for [place]" / "is [restaurant] open" — full details for a Google Place: address, phone, hours, website, ratings, user reviews. Requires a place ID from `maps_place_search`. Use after search to drill into one specific business.

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• principles.search

  AI Design Blueprint Doctrine

  Search Blueprint principles by free-text query and return the closest matches ranked by relevance. Use this to find principles related to a specific design challenge, failure mode, or keyword (e.g. 'reversibility', 'approval flow', 'delegation boundary'). Returns principle title, cluster, definition, rationale, and implementation heuristics. Prefer this over principles.list when you have a specific topic in mind rather than wanting all principles.

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• seo_keyword_research

  gapup-mcp

  SEO keyword research from a seed keyword or topic. Uses Google Suggest (public, keyless) to discover related queries at 2 expansion levels, then clusters them by intent: informational / commercial / transactional / navigational — via heuristic pattern matching. Search volume is bucketed (very_high / high / medium / low / very_low) and clearly labelled as ESTIMATED — no fabricated precise numbers. Returns all keywords, intent clusters, quality scores (0-100), and top 10 opportunities. Supports country (gl) and language (hl) targeting. 100% keyless. Cache TTL 6h. ICP: SEO managers, content strategists, SaaS founders, agency teams.

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• google_ads_list_accessible_customers

  paid-ads

  Lists directly accessible Google Ads customers for the configured Google Ads credentials, including descriptive names when Google returns them. Use this to discover customer IDs before running Google Ads hierarchy or reporting tools.

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• get_keyword_performance

  RevenueScope: Japanese EC RPS Benchmarks

  Return search-query performance from Google Search Console for the given period. band='all' (default) returns per-query metrics — clicks/impressions/CTR/avg position/top landing page plus an estimated revenue per query (= 検索 organic RPS × clicks, a conservative estimate, 0 until the site has 検索 organic revenue), ranked by clicks (default limit 100). Each row also carries the period-over-period change vs the previous equal-length window: clicks_change (traffic) and est_revenue_change (money), both % deltas (null = the query is NEW, i.e. had no clicks/revenue last period — render as '新規', not 0%). Comparing the two surfaces RS's signature insight — e.g. clicks +74% but est_revenue −21% means traffic grew while money fell, something GA4/GSC cannot show side by side. band='striking' returns the SEO action list: queries 'striking distance' from the top (ranking ~4-20 with real impressions) where improving a few positions yields the biggest click/revenue gain, ranked by estimated revenue opportunity (incremental clicks × search-organic RPS, default limit 10); the methodology is fixed in code. site_id is OPTIONAL when OAuth-authenticated. Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). Google-search only.

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• search_google_xray

  LinkedIn MCP Server (Salesbot)

  Google X-Ray search for public LinkedIn profiles via Google operators (site:linkedin.com/in). Useful when you don't want to consume LinkedIn search limits. Found profiles are saved into your contacts (in a 'Google X-Ray' list, deduplicated by profile URL) and the tool returns their contact_id values. To move them into the CRM, add them to a campaign with add_contacts_to_campaign (auto-creates CRM leads) or use a CRM tool like set_deal_stage. Paginates Google results and auto-filters duplicates.

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• google-trends.regions

  Vee3

  List all countries and subregions you can pass to other Google Trends tools in the country and region fields. Returns geo.countries: each country name maps to country (label) and regions (array of subregion names). Also returns msg. Use this before interest-over-time or interest-by-region calls when filtering by geography. Pair with google-trends.categories when filtering by category. Cost = 5 tokens.

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• google-trends.related_queries

  Vee3

  Fetch Google Trends related queries for one to five keywords. Returns a JSON object whose top-level keys are your keywords. Each value has top and rising sections; each section has query (rank index to query string) and value (rank index to score). Requires start in datetime-with-timezone form (for example 2020-05-01T00:43:37+0100). Optional end defaults to now. country defaults to global; region requires a valid country. category and gprop default to all when omitted or empty. Use google-trends.categories and google-trends.regions to discover valid category, country, and region values. Cost = 40 tokens.

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• serpapi_google_shopping

  Serpapi

  Search Google Shopping for `<query>` — returns product title, price, source, rating, reviews, product ID, and link via SerpApi. Example: serpapi_google_shopping({ q: "wireless earbuds", gl: "us", hl: "en", _apiKey: "your-serpapi-key" })

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