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

• search_papersA

  SemanticscholarA

  Search 200M+ academic papers on Semantic Scholar by keyword or exact title. Returns titles, authors, year, venue, CITATION COUNTS, DOI, and open-access PDF links. PREFER for "how many citations does <paper title> have", "citation count for <paper>", "how cited is <paper>" — search the title and read citationCount off the match. Optionally filter by year range and field of study. Keyless.

  ConnectorNo auth

• google_scholarA

  SearchapiA

  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.

  ConnectorOAuth

• hasdata_google_scholar_cite_getScholarCitationFormatsA

  HasDataA

  Get Scholar Citation Formats Looks up citation formats and export links for a single Google Scholar organic search result, identified by its `resultId` (as returned in a google/scholar organic result). Returns formatted citation snippets (MLA, APA, Chicago, Harvard, Vancouver) and reference-manager export links (BibTeX, EndNote, RefMan, RefWorks). Use to build citation/bibliography features or complete a research workflow started with google/scholar.

  ConnectorAPI key

• hasdata_google_scholar_cite_getScholarCitationFormatsA

  HasDataA

  Get Scholar Citation Formats Looks up citation formats and export links for a single Google Scholar organic search result, identified by its `resultId` (as returned in a google/scholar organic result). Returns formatted citation snippets (MLA, APA, Chicago, Harvard, Vancouver) and reference-manager export links (BibTeX, EndNote, RefMan, RefWorks). Use to build citation/bibliography features or complete a research workflow started with google/scholar.

  ConnectorAPI key

• hasdata_google_scholar_cite_getScholarCitationFormatsA

  Google Scholar Remote MCP ServerA

  Get Scholar Citation Formats Looks up citation formats and export links for a single Google Scholar organic search result, identified by its `resultId` (as returned in a google/scholar organic result). Returns formatted citation snippets (MLA, APA, Chicago, Harvard, Vancouver) and reference-manager export links (BibTeX, EndNote, RefMan, RefWorks). Use to build citation/bibliography features or complete a research workflow started with google/scholar.

  ConnectorAPI key

• search_bibtexA

  xpay Academic Research CollectionC

  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.

  ConnectorNo auth

• search_google_scholarA

  xpay Academic Research CollectionC

  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.

  ConnectorNo auth

• search_semanticA

  xpay Academic Research CollectionC

  Search academic papers from Semantic Scholar. Args: query: Search query string (e.g., 'machine learning'). year: Optional year filter (e.g., '2019', '2016-2020', '2010-', '-2015'). max_results: Maximum number of papers to return (default: 10). Returns: List of paper metadata in dictionary format.

  ConnectorNo auth

• serpapi_google_scholarA

  SerpapiA

  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" })

  ConnectorNo auth

• search_scholarA

  WebLensB

  Google Scholar academic search. Returns papers with snippets, publication info, and citation counts. Price: $0.005

  ConnectorNo auth

• search_papersB

  tollboothC

  Search 226M+ academic papers across all fields with AI-ranked relevance via Semantic Scholar Graph API.

  ConnectorNo auth

• search_papersC

  LaTeX-AgentB

  Search academic papers via OpenAlex (default) or Semantic Scholar. Use source="semantic_scholar" for broader AI/CS preprint coverage and citation-influence metrics (#3596).

  ConnectorAPI key

• search_semanticC

  adamamer20-paper-search-mcp-openaiC

  Search academic papers from Semantic Scholar.

  ConnectorOAuth

• search_google_scholarC

  adamamer20-paper-search-mcp-openaiC

  Search academic papers from Google Scholar.

  ConnectorOAuth

• 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

• amazon_get_keyword_metricsA

  Manifold MCPA

  Monthly Amazon search volume for up to 1,000 keywords you already have, in the order given. Use when: "how many people search Amazon for X", comparing Amazon demand with Google demand, sizing a product idea on Amazon. Not for: Google volume, use seo_get_keyword_metrics. Finding keywords, use amazon_search_keywords. Returns: List. rows[]: keyword, volume (monthly Amazon searches; null = no data). Cost: 5 + 5 per 100 keywords. 10 keywords = 6 credits. Cached 7 days. Example: {"keywords": ["standing desk", "desk converter"]}

  ConnectorAPI key

• get_sub_domainsA

  AnySearchA

  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: academic agriculture business code energy environment film finance gaming health ip legal resource security social_media travel ## 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.

  Connector

  Destructive

  No auth

• get_research_papersA

  TensorFeedA

  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.

  ConnectorNo auth

• search_papersA

  Scholar FeedA

  Search Scholar Feed's 600k+ CS/AI/ML paper corpus. Semantic (embedding) search by default, so it finds conceptually related work even when the wording differs. EVERY PARAMETER DOCUMENTS ITS OWN BEHAVIOUR AND COVERAGE LIMITS — read the ones you intend to use; this description covers only what no single parameter can tell you. RETRIEVAL LIMIT: semantic ranking favours recent, stylistically-matched papers and routinely MISSES the old high-citation anchor of a field (H2O for KV eviction, GRIT for unified embedding+generation). To reach a field's canonical work, read the top-5 abstracts for repeated baseline mentions ('we compare against X') and look that name up directly, or call get_foundational_lineage. THREE UNRELATED NOTIONS OF IMPACT, easily confused: proven citations (sort='impactful', min_citations) | a ~90-day forecast percentile that is NULL on older papers and therefore excludes them (sort='trending', impact_min) | GitHub adoption (sort='community', min_stars). YOUR LIBRARY IS MARKED INLINE on authenticated calls: each hit carries is_saved and is_read, and a hit you previously annotated carries note_text — your own earlier verdict. Read note_text INSTEAD of re-deriving a conclusion from the abstract; re-judging a paper you already ruled on is the most common way an agent wastes a research session. is_saved=false is a real measurement; on anonymous calls these keys are absent entirely, so never read a missing is_saved as false. Papers new to you are ranked exactly as before — nothing is demoted for being unseen.

  ConnectorNo auth

• ask_pipeworxA

  Jpl SsdA

  PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 7,133 tools across 1853 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1". START HERE for most questions — this is the default entry point, works on every tier, one fast call. Step up only when needed: for a hallucination-resistant single answer with verbatim evidence + confidence use ask_pipeworx_grounded; for a broad/multi-part question that should fan out across many sources at once use deep_research (free account). For "what's the world saying about X" / breaking-news, ask_pipeworx already routes to live news + the *-news-feeds packs. Optionally pass packs:["slug","slug"] to restrict routing to specific sources ("answer this using THESE packs"); omit it for full-catalog routing.

  ConnectorNo auth

• ask_pipeworxA

  NihrA

  PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 7,133 tools across 1853 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1". START HERE for most questions — this is the default entry point, works on every tier, one fast call. Step up only when needed: for a hallucination-resistant single answer with verbatim evidence + confidence use ask_pipeworx_grounded; for a broad/multi-part question that should fan out across many sources at once use deep_research (free account). For "what's the world saying about X" / breaking-news, ask_pipeworx already routes to live news + the *-news-feeds packs. Optionally pass packs:["slug","slug"] to restrict routing to specific sources ("answer this using THESE packs"); omit it for full-catalog routing.

  ConnectorNo auth