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550,187 tools. Updated 2026-09-11 18:57

"How to search for academic papers using Google Scholar" matching MCP tools:

  • 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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  • Search Google Scholar for computer science research papers, citations, and academic publications. Returns paper title, authors, publication details, citation count, and link to paper. Use for finding research on CS topics, reviewing state-of-the-art, or citation tracking.
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  • Search Google Scholar for computer science research papers, citations, and academic publications. Returns paper title, authors, publication details, citation count, and link to paper. Use for finding research on CS topics, reviewing state-of-the-art, or citation tracking.
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  • Search for academic papers and return BibTeX citations. Searches DBLP (computer science) and Semantic Scholar (broad academic coverage) for comprehensive results. Returns formatted BibTeX entries ready to use in LaTeX documents.
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  • Search academic papers from Google Scholar. Args: query: Search query string (e.g., 'machine learning'). max_results: Maximum number of papers to return (default: 10). Returns: List of paper metadata in dictionary format.
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  • 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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Matching MCP Servers

  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables agents to search, analyze, and explore arXiv academic papers with advanced multi-field search, author lookup, category browsing, citation extraction, and bibliography export.
    26
    Apache 2.0
  • A
    license
    Not graded
    quality
    C
    maintenance
    An MCP server for searching Google Scholar, enabling paper search, author lookup, citation tracking, and BibTeX export for AI assistants and automation workflows.
    2
    MIT

Matching MCP Connectors

  • Search Google Scholar for academic papers, citations, and author profiles.

  • google search: google web search api, web, images, videos, news, music, favicon, proxy, audio.

  • 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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  • Search Google Scholar for academic papers on `<topic>` — returns title, link, snippet, publication info, and citation count via SerpApi. Example: serpapi_google_scholar({ q: "graph neural networks", as_ylo: 2020, num: 10, _apiKey: "your-serpapi-key" })
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  • Search Google Scholar for academic papers with snippets, publication info, and citation counts. Price: $0.015
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  • Search 226M+ academic papers across all fields with AI-ranked relevance via Semantic Scholar Graph API.
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  • Search quantum computing research papers from arXiv. Use when the user asks about recent research, specific papers, or academic topics in quantum computing. NOT for jobs (use searchJobs) or researcher profiles (use searchCollaborators). Supports natural language queries decomposed via AI into structured filters (topic, tag, author, affiliation, domain). Date range defaults to last 7 days; max lookback 12 months. Returns newest first, max 50 results. Use getPaperDetails for full abstract and analysis of a specific paper. Examples: "trapped ion papers from Google", "QEC review papers this month", "quantum error correction".
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  • 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".
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  • This is Anysearch's domain discovery tool. IMPORTANT: Step 1 of vertical search. REQUIRED before any search that uses a domain. Returns valid sub_domains and sub_domain_params for the specified domain(s). Call this when the query targets a specialized vertical or needs structured parameters: stock prices, financial data, academic papers, legal cases, medical/drug info, flight status, weather, exchange rates, geographic POIs, code repositories, or any domain where a structured identifier (ticker, DOI, CVE, IATA, coordinates) is involved. ## When to call — pick the domain(s) that match what the user is asking about: resource social_media finance academic legal health business security ip code energy environment agriculture travel film gaming ## Input — choose from the list above and pass via the domain or domains parameter: - domain: single domain string (use only when 100% certain the query is single-domain) - domains: batch query for up to 5 domains in one call (takes priority over domain) 🏆 ALWAYS prefer the `domains` (plural, array) parameter. Pass ALL potentially relevant domains at once — even for seemingly single-domain queries, consider related domains: - Query about "cryptocurrency regulations" → domains=["finance", "legal", "security"] - Query about "best gaming laptops" → domains=["gaming", "tech", "ecommerce"] - Query about "climate change impact on agriculture" → domains=["environment", "energy", "academic"] ## Returns Markdown table filtered to the specified domains: sub_domain | description | params ## CRITICAL: How to use results - sub_domain is the PRIMARY routing key — always pass it to search - params column shows available structured parameters — pass them via sub_domain_params in search, NEVER embed in query - If multiple sub_domains returned (especially from multiple domains), use batch_search — one query per sub_domain — instead of multiple sequential search calls - Params marked (required) in the output MUST be passed when using that sub_domain in search. If a required param is not applicable to your query, pass it as an empty string (key: "") — do not skip it.
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  • General-purpose Google search — returns organic results for any query. Unlike search_google_xray (LinkedIn-only), this searches the entire web. Useful for finding job postings on portals (jobs.cz, prace.cz, profesia.sk, indeed.com), company info, news, or any other web content. Results are NOT saved to contacts — use this for research and discovery. Capped at 4 calls per minute to protect the Serper/Google budget.
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  • Map the conceptual landscape around a topic ACROSS THE PAPER CORPUS. Searches papers and their chunks, not the layer-2 claim graph — for published CLAIMS on a topic use methodist_explore_topic. Instead of returning a ranked list of papers, returns N distinct conceptual clusters with representative chunks. Built on keyConcept LLM-extracted markers diversification. Use for "what approaches exist to X" queries — answers with thematic map rather than ranked list. Better than search when you want breadth over depth. Temporal bias note: for topics with dense recent literature (e.g. current LLM research), the default ordering favors recent papers because vector similarity finds them first; specify dateTo for historical exploration of mature topics, or dateFrom+dateTo to slice a specific era. Diversification cap (maxClustersPerPaper) limits how many clusters can have the same source paper as representative chunk — protects against single-paper dominance.
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  • Get recent AI/ML research papers from one of three feeds, chosen with the source argument. arxiv_recent is the firehose: newest arXiv submissions in cs.AI / cs.LG / cs.CL / cs.CV by submission date, refreshed daily at 11:30 UTC. trending is citation-ranked from Semantic Scholar across five fan-out queries, deduped, refreshed daily at 11:00 UTC. hf_daily is Hugging Face editor-curated with community upvotes and discussion counts, refreshed daily at 14:15 UTC. Pick arxiv_recent for what is brand new, trending for what is influential, hf_daily for what practitioners are discussing. License: arXiv and Semantic Scholar permit metadata use; the standard attribution block ships on every response.
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  • Google Ads (Keyword Planner) monthly search volume, CPC, competition, 12-month trend and keyword difficulty (0–100, DataForSEO Labs) for up to 10 keywords in one call. Use when the user asks how often something is searched or how hard it is to rank, or to size a keyword before writing a page. Omit location_code for worldwide volume (difficulty is then computed for the US), or pass a market code for one country. Costs credits; the same keyword set re-queried within a week is free.
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