"Using Google Search to Find Answers" matching MCP tools:

• proximens_oracle_search_principles

  Proximens Oracle

  Semantic search over the Proximens GEO Oracle: a curated, continuously-updated knowledge base of 3.000+ verified Generative Engine Optimization (GEO/AEO) principles, each graded by a 0-1 confidence score and traceable to a verified source. INPUT: query (natural language, 3-500 chars); optional category (one of 13 GEO categories), top_k (1-25, default 10), min_confidence (0-1, default 0.5). RETURNS: ranked principles as JSON, each with id, title, summary, category, confidence and a relevance score; Pro/Enterprise tiers additionally return full_text and source. USE WHEN you need evidence-backed answers about how AI search engines (ChatGPT, Perplexity, Gemini, Google AI Overviews, Copilot) select, rank and cite web content.

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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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• find_matches_in_index

  RChilli MCP Hub

  Match one source document against the user's ALREADY-INDEXED corpus and return the best-matching, ranked candidates (RChilli Search & Match Engine). Requires a populated index. Uses RChilli's purpose-built matching engine — more reliable than manually comparing documents. Use this when the user wants to: find the best/top matching resumes for a JD, find matching candidates from their pool, or rank their indexed resumes/JDs against a given document — e.g. "find the best candidates in my database for this job". Also phrased as: shortlist from my pool, top matches for this JD, rank my candidates. Do NOT use for: scoring a single resume against a single JD with no index (use ``search_one_match``); plain keyword lookup (use ``search_simple_search``). Supports all four match directions by combining ``index_type`` and ``doc_type``: - **JD to Resume** — ``index_type='Resume'``, ``doc_type='JD'``: Search the Resume index using a JD as the source document. - **Resume to Resume** — ``index_type='Resume'``, ``doc_type='Resume'``: Search the Resume index using a Resume as the source document. - **Resume to JD** — ``index_type='JD'``, ``doc_type='Resume'``: Search the JD index using a Resume as the source document. - **JD to JD** — ``index_type='JD'``, ``doc_type='JD'``: Search the JD index using a JD as the source document. The ``document_text`` is automatically parsed using the RChilli Resume or JD parser (driven by ``doc_type``), and the resulting structured JSON is base64-encoded and submitted as the match source — no manual encoding is required. Args: index_type: Index to search — ``Resume`` (default) or ``JD``. index_key: Same as ``userkey`` — the RChilli API user key. Leave blank; the authenticated session userkey is injected automatically. doc_type: Type of the source document — ``Resume`` (default) or ``JD``. This determines which parser processes ``document_text``. document_text: Plain-text content of the source document. Parsed and encoded to base64 JSON internally.

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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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• google_rank_tracking

  Searchapi

  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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• google_scholar

  Searchapi

  Search Google Scholar for academic papers, citations, and scholarly articles. Returns results with titles, authors, publication info, citation counts, and links to PDFs. Use cites parameter to find papers citing a specific work, or cluster to find all versions of a paper. For US court opinions and case law, use google_scholar_cases instead.

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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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• maps_directions

  Google_maps

  "Google Maps directions from A to B" / "transit / public-transport directions" / "bus / subway / train route" / "best way to get from [X] to [Y]" — turn-by-turn directions via Google Maps. Modes: driving, walking, transit (bus/subway/train), bicycling. Requires Google Maps API key. PREFER over Mapbox/OpenRouteService specifically for public-transit routing — Google has the best transit data.

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• emem_benchmark

  emem — Earth memory protocol

  Hand-verified evaluation items for grading an agent against the responder. Returns {items[], grader_url}. Submit answers (cell64 or fact_cid per item) to POST /v1/benchmark/grade for per-item scores. Items today: elevation recall, NDVI, find_similar neighbours. When to use: Call once at agent-onboarding time (or in CI) to fetch the canonical task list, then have the agent answer each item using its normal tool routing, and POST the answers map to /v1/benchmark/grade for a deterministic score. Lets an operator regression-check that an agent build still hits ground truth.

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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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• request_oauth_integration_url

  clariBI.com

  Initiate an OAuth handoff to a vendor integration (Google Ads, GA4, Search Console, Sheets, Drive, BigQuery, Meta Ads, Jira, Confluence). Returns an authorization URL the user opens in a browser. After the user clicks Allow, the connection is created and you can poll check_integration_status(handoff_id) to find out when the data is ready.

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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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• firecrawl_map

  firecrawl-mcp

  Map a website to discover all indexed URLs on the site. **Best for:** Discovering URLs on a website before deciding what to scrape; finding specific sections or pages within a large site; locating the correct page when scrape returns empty or incomplete results. **Not recommended for:** When you already know which specific URL you need (use scrape); when you need the content of the pages (use scrape after mapping). **Common mistakes:** Using crawl to discover URLs instead of map; jumping straight to firecrawl_agent when scrape fails instead of using map first to find the right page. **IMPORTANT - Use map before agent:** If `firecrawl_scrape` returns empty, minimal, or irrelevant content, use `firecrawl_map` with the `search` parameter to find the specific page URL containing your target content. This is faster and cheaper than using `firecrawl_agent`. Only use the agent as a last resort after map+scrape fails. **Prompt Example:** "Find the webhook documentation page on this API docs site." **Usage Example (discover all URLs):** ```json { "name": "firecrawl_map", "arguments": { "url": "https://example.com" } } ``` **Usage Example (search for specific content - RECOMMENDED when scrape fails):** ```json { "name": "firecrawl_map", "arguments": { "url": "https://docs.example.com/api", "search": "webhook events" } } ``` **Returns:** Array of URLs found on the site, filtered by search query if provided.

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• search_files

  vibedeploy

  Search for a literal string or basic regex across all files in either the served dist or the editable source tree. Use this BEFORE batch-reading files to find candidates — saves the 'read 14 batches just to find which 3 files matter' round trip. Pass `target: "source"` to search the editable tree (requires Site.sourceStored=true).

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• microsoft_docs_search

  Microsoft Learn MCP

  Search official Microsoft/Azure documentation to find the most relevant and trustworthy content for a user's query. This tool returns up to 10 high-quality content chunks (each max 500 tokens), extracted from Microsoft Learn and other official sources. Each result includes the article title, URL, and a self-contained content excerpt optimized for fast retrieval and reasoning. Always use this tool to quickly ground your answers in accurate, first-party Microsoft/Azure knowledge. ## Follow-up Pattern To ensure completeness, use microsoft_docs_fetch when high-value pages are identified by search. The fetch tool complements search by providing the full detail. This is a required step for comprehensive results.

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• tourradar_vertex-review-search

  MolTravel

  [tourradar] Search tour reviews using AI-powered semantic search. Requires tourIds to scope results to specific tours. Use this when the user asks about reviews, feedback, or experiences for specific tours. Combine with an optional text query to find reviews mentioning specific topics (e.g., 'food', 'guide', 'accommodation'). When you don't have tour IDs, use vertex-tour-search or vertex-tour-title-search first to find them.

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• search

  Koalr

  Search across all Koalr entities: developers (by name or GitHub login), repositories (by name), pull requests (by title or branch), and teams (by name). Use this when you need to find an entity before using a more specific tool. Read-only.

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• search_series

  FRED Economic Data

  Search for FRED economic data series by keyword. Use this to find series IDs for economic indicators. For example, search 'unemployment rate' to find UNRATE, or 'gross domestic product' to find GDP. Returns series metadata including ID, title, frequency, units, and date range. Common series: UNRATE (unemployment), GDP (gross domestic product), CPIAUCSL (consumer price index), FEDFUNDS (federal funds rate), MORTGAGE30US (30-year mortgage rate), MEHOINUSA672N (median household income). Args: search_text: Keywords to search for (e.g. 'unemployment rate', 'GDP', 'inflation'). limit: Maximum number of results to return (default 10, max 1000).

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• answer_query

  mcp

  Use answer_query to get a grounded answer to a query about Google developer products. This tool has limited quota. This tool will synthesize information from the corpus to generate an answer to the query. answer_query grounds answers using the same corpus as search_documents. This tool returns the generated answer_text and a list of document names (references) used to generate the answer. Use get_documents with the document names to fetch the entire document content if needed. If you get a 429 out of quota error, use search_documents instead.

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• axint.registry.search

  Axint

  Search the Axint Registry for already-published packages that match a natural-language query. Use this BEFORE calling axint.feature or axint.compile so the agent can install an existing package instead of... Use: use before generating code to find reusable packages; not for validating local Swift. Effects: read-only local registry search using AXINT_REGISTRY_PATH or sibling checkout; no network by default.

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