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251,226 tools. Last updated 2026-06-30 13:07

"Using Bing to Generate Search Results" matching MCP tools:

  • Use this when the user wants to discover the canonical marketing reporting graph, available sources, supported metrics, supported dimensions, or which connectors are live today. Each source also reports a `passthrough` field describing whether native fields beyond the curated list are accepted (GA4 accepts any native dimension/metric; Search Console accepts any native dimension; Bing is limited to the curated fields). Do not use this for GA4 account discovery or data retrieval.
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  • Multi-language, multi-source web search that goes beyond Anglo-centric results. Supports 15 languages (fr/de/es/it/pt/nl/ja/zh/ko/ar/ru/sv/pl/tr/en) with automatic detection. Aggregates results from Mojeek (independent search engine, multilang) and Wikipedia (native multilang API), with DDG and HN as English-language complements. Returns deduplicated results ranked by cross-engine consensus. Use when you need non-English search results, when DDG fails, or for geographically-biased queries. Phase 2 #7 of the geo/lang expansion plan. Note: Brave/Bing/Searx are blocked from DO IPs — configure AICI_RESEARCH_PROXY_URL for residential proxy.
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  • 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 the regulatory corpus using keyword / trigram matching. Uses PostgreSQL trigram similarity on document titles and summaries. Returns documents ranked by relevance with summaries and classification tags. Prefer list_documents with filters (regulation, entity_type, source) first. Only use this for free-text keyword search when structured filters aren't sufficient. Args: query: Search terms (e.g. 'strong customer authentication', 'ICT risk', 'AML reporting'). per_page: Number of results (default 20, max 100).
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  • Get Google organic search results for SEO rank tracking. Returns up to 100 results per request with position, title, URL, and snippet. Ideal for monitoring keyword rankings and SERP analysis.
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  • Multi-language, multi-source web search that goes beyond Anglo-centric results. Supports 15 languages (fr/de/es/it/pt/nl/ja/zh/ko/ar/ru/sv/pl/tr/en) with automatic detection. Aggregates results from Mojeek (independent search engine, multilang) and Wikipedia (native multilang API), with DDG and HN as English-language complements. Returns deduplicated results ranked by cross-engine consensus. Use when you need non-English search results, when DDG fails, or for geographically-biased queries. Phase 2 #7 of the geo/lang expansion plan. Note: Brave/Bing/Searx are blocked from DO IPs — configure AICI_RESEARCH_PROXY_URL for residential proxy.
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Matching MCP Servers

Matching MCP Connectors

  • Brave Search MCP — independent web index (no Google/Bing dependency)

  • Search PubMed and summarize biomedical literature — designed for AI health agents.

  • AXIS-owned BM25 search engine over the corpus YOUR account has indexed. NOT a Google/Bing scraper — agents build their own searchable index by first calling operation='index' with documents (often pages fetched via iliad_web_research), then querying with operation='search'. Five operations: `index` (insert one or many documents), `search` (BM25 top-k ranked hits with snippet + score + metadata), `delete` (drop one doc), `delete_namespace` (drop all), `count`. Namespaces are account-scoped server-side (`acct:<id>:<namespace>`). Persistent across restarts via SQLite. Search supports `max_results` (default 10, max 100) and `site` (restrict to a single URL host, case-insensitive). Engineer mode (X-Agent-Mode: engineer — Answer Engine, $0.25): search also returns a grounded extractive answer with [n] citation spans over your corpus, reranked, refusing on weak evidence. Requires Authorization: Bearer <api_key>.
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  • Search across ALL string properties of ALL nodes in a deployed graph using free-text queries. Unlike search_graph_nodes (which filters by specific property), this searches every text field at once. Perfect for finding knowledge when you don't know which property contains the answer. Example: query "quantum" searches name, description, summary, notes, and all other string fields. Returns nodes with _match_fields showing which properties matched. Optionally filter by entity_type to narrow results.
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  • Performs web searches using the Brave Search API and returns comprehensive search results with rich metadata. To chain into local-POI enrichment, pass `result_filter=locations` and feed the resulting `locations.results[].id` values into `brave_local_search`. To chain into the AI summarizer, pass `summary=true` and feed the returned `summarizer.key` into `brave_summarizer`.
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  • Retrieve results from a previously executed SDK job using the resultId from `sdk-query-execute`. If the query is complete, returns results immediately. If still pending, polls for up to 1 more minute. Use this after `sdk-query-execute` returns PENDING status.
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  • MIXED search across all 21,090 opportunities from 581 sources — combines grants AND procurement/contracts in one query. This mixes funding types which can create noisy results. ROUTING — use the specialized tool instead: • User wants grants/funding/fellowships/prizes → search_grantsplus • User wants contracts/RFPs/procurement/bids → search_procurement • User explicitly wants BOTH types → search_grants_and_procurement • Intent unclear → ASK the user first Use search_grantsplus or search_procurement for cleaner, more relevant results. Free tier: 10 searches/month, up to 10 results each. Paid: full results, up to 100 per search.
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  • Search O*NET occupations by keyword. Returns a list of occupations matching the keyword with their SOC codes, titles, and relevance scores. Use the SOC code from results with other O*NET tools to get detailed information. Args: keyword: Search term (e.g. 'software developer', 'nurse', 'electrician'). limit: Maximum number of results to return (default 25).
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  • Search for icons by keyword across all collections. Returns icon names in prefix:name format (e.g., "mdi:home"). Use get_icons to fetch SVG data for results.
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  • Research any topic — search Google, Bing, YouTube, X/Twitter, Amazon, Yelp, Google Trends, news, and 100+ more engines. Read webpages, extract video transcripts, find reviews, track competitors. Works without a domain.
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  • Search campervan and motorhome rentals. Returns a URL that pre-fills the search form with your trip details. Click Search on the page to see live results with pricing, availability, and booking options from 160+ rental companies.
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  • Generate a structured, GEO-optimized content brief for a topic using the Proximens Oracle. INPUT: topic (3-200 chars); optional target_branche (one of 7 verticals), word_count_target (300-5000, default 1500) and up to 3 competitor_urls. RETURNS: JSON with a suggested H1 and H2 section structure with key points, the principles the content should address, and (Pro/Enterprise) FAQ suggestions and recommended schema.org markup. USE WHEN you need to brief a writer so a page is built to be cited by AI search engines.
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  • Generate a WhatsApp inquiry link for a Fursat home. Returns a wa.me URL that opens WhatsApp with a prefilled message identifying the listing and (optionally) the guest's dates, party size, and notes. Use this when a traveler wants to actually book or inquire about a specific home after seeing it in search results. Bookings on Fursat happen on WhatsApp — there is no online checkout.
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  • Query a RAG collection using natural language to retrieve relevant document chunks. Performs semantic search over the collection's indexed documents and returns the most relevant chunks ranked by similarity. Optionally synthesizes an AI-generated answer using the retrieved context. Parameters: - query: Natural language question or search phrase - top_k: Number of chunks to retrieve (default 5, max 20) - threshold: Minimum similarity score 0-1 (only return chunks above this score) - synthesize: If true, uses an LLM to generate a natural language answer from the retrieved chunks (default false — returns raw chunks only) - model: LLM model to use for synthesis (only relevant when synthesize is true, default: anthropic/claude-haiku-4.5) - filter: Metadata filter to narrow results (e.g. { category: "faq" }) Example — raw retrieval: Input: { app_id: "app_abc123", collection: "knowledge-base", query: "How do I reset my password?", top_k: 3 } Output: { chunks: [ { text: "To reset your password, go to Settings > Security > Reset Password...", score: 0.92, document_id: "doc_abc", metadata: { category: "faq", source: "help-center" } }, ... ] } Example — with synthesis: Input: { app_id: "app_abc123", collection: "knowledge-base", query: "How do I reset my password?", top_k: 5, synthesize: true } Output: { answer: "To reset your password, navigate to Settings > Security and click...", chunks: [ ... ], model: "gpt-4o-mini" } Example — with metadata filter: Input: { app_id: "app_abc123", collection: "knowledge-base", query: "pricing plans", filter: { category: "billing", version: "2.0" } } Use this to: - Search documentation or knowledge bases using natural language - Build AI-powered Q&A features for end users - Find relevant context for AI assistants - Power search bars with semantic understanding Common errors: - RESOURCE_NOT_FOUND: App or collection doesn't exist - COLLECTION_EMPTY: No documents have been ingested yet Idempotency: Safe to call anytime (read-only operation).
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  • Search the Fonto XML documentation using full-text search. Returns results ranked by relevance with titles, descriptions, and slugs. Best for looking up a concept, API name, or feature by keyword.
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  • Full-text search across a MusicBrainz entity type (artist, release-group, release, recording, work, label) using a Lucene query string. Returns ranked matches with MBID, name/title, disambiguation, type, and a 0–100 relevance score (100 = exact). Starting point when resolving a name to an MBID — chain the returned MBID into the matching musicbrainz_get_* tool. Results are in MusicBrainz score-descending order. Supports field-scoped Lucene syntax (e.g. `artist:radiohead AND country:GB`).
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