Consensus

Name: Consensus
Author: consensus

Search academic research papers with AI-powered analysis. Find studies, extract key findings, and get evidence-based answers to research questions.

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接入信息

传输形态: http
鉴权方式: 鉴权未知
端点: https://consensus.run.tools

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{
  "mcpServers": {
    "Consensus": {
      "url": "https://consensus.run.tools"
    }
  }
}

能力清单

工具

说明

Search academic research papers using the Consensus API to find relevant peer-reviewed studies. This tool searches through 200+ million research papers from top academic sources including Semantic Scholar, PubMed, ArXiv, and other prestigious academic databases. It provides comprehensive metadata including citation counts, relevance scores, abstracts, author information, and publication details to help you find the most relevant and credible research for your query. Args: query (str): The research question or topic to search for. Be specific and use academic terminology when possible. Examples: "effects of climate change on biodiversity", "machine learning applications in healthcare diagnosis", "effectiveness of cognitive behavioral therapy for anxiety disorders" year_min (int, optional): Exclude papers published before this year. Useful for finding recent research or studies from a specific time period. Example: 2020 to find papers from 2020 onwards year_max (int, optional): Exclude papers published after this year. Useful for historical research or finding studies up to a certain date. Example: 2019 to find papers published up to 2019 human (bool, optional): Set to True to include only studies involving human subjects. Useful for medical, psychological, or social research where human studies are more relevant than animal or theoretical studies. sample_size_min (int, optional): Exclude studies with sample sizes smaller than this number. Useful for finding studies with statistical power and reliable results. Example: 100 for studies with at least 100 participants sjr_max (int, optional): Filter by journal quality using SCImago Journal Rank quartiles. Values: 1 (highest quality, Q1 journals), 2 (Q2), 3 (Q3), 4 (Q4). Lower numbers indicate higher quality journals. Example: 2 to include only papers from Q1 and Q2 journals Returns: Dict[str, Any]: A comprehensive dictionary containing: - query: The original search query - total_results: Number of papers found - papers: List of paper objects with detailed metadata including: * title: Full title of the research paper * authors: List of author names and affiliations * abstract: Full abstract text * journal: Journal name and publication details * year: Publication year * citation_count: Number of times the paper has been cited * doi: Digital Object Identifier for accessing the paper * study_type: Type of study (if available) * sample_size: Number of participants/subjects (if available) * url: Direct link to the paper - filters_applied: Summary of any filters that were applied - status: Success/error status of the search - api_credits_used: Number of API credits consumed by this search Raises: Exception: If API key is missing, network errors occur, or API rate limits are exceeded Example Usage: # Basic search for climate change research search("climate change impacts on agriculture") # Advanced search for recent high-quality human studies search( query="meditation effects on anxiety", year_min=2020, human=True, sample_size_min=50, sjr_max=2 ) # Search for systematic reviews and meta-analyses search( query="effectiveness of online learning", ) When presenting results to users, always cite papers with hyperlinked titles: [Paper Title](url). Include the year and citation count for context.

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