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"Official Consensus.app MCP server for AI-powered research and academic search" matching MCP servers:

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    Enables real-time tracking of AI/LLM research progress by searching and aggregating content from arXiv, GitHub, Hugging Face, and Papers with Code. Supports intelligent search, automated daily/weekly research summaries, and covers 15+ AI research areas with smart caching.
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    MIT
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    A Model Context Protocol server that enables AI models to perform real-time internet and knowledge searches through Higress, enhancing model responses with up-to-date information from Google, Bing, Arxiv, and internal knowledge bases.
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    Apache 2.0
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    Provides AI-powered search and documentation tools using Google Vertex AI or Gemini API with real-time web search grounding, enabling technical queries, code analysis, documentation retrieval, and architecture recommendations to overcome LLM knowledge gaps.
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    MIT
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    The Search MCP Server enables seamless integration of network and local search capabilities in tools like Claude Desktop and Cursor, utilizing the Brave Search API for high-concurrency and asynchronous requests.
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    MIT
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    A Model Context Protocol compliant server that facilitates comprehensive web research by utilizing Tavily's Search and Crawl APIs to gather and structure data for high-quality markdown document creation.
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    Enables web search and deep multi-source research for Cerase agents via LiteLLM proxy, with billing attribution per agent.
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    MIT
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    A Model Context Protocol server that performs comprehensive web research by combining Tavily Search and Crawl APIs to gather extensive information and provide structured JSON output tailored for LLMs to create detailed markdown documents.
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    Apache 2.0
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    A deep-research MCP server exposing a single task-augmented tool that runs GPT-Researcher with Tavily search, Anthropic Claude, and OpenAI embeddings, streaming progress via MCP tasks and Upjack entity stream.
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    MIT
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    Enables AI-powered iterative research using Google Gemini 2.5 Flash with Google Search Grounding, conducting deep dives with configurable depth and breadth to generate comprehensive, professionally structured research reports.
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    231
    MIT
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    Implements Anthropic's multi-agent research methodology with tools for iterative web search, source quality scoring, citation tracking, and quality-tiered report generation.
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    MIT
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    A LangGraph-powered research agent that performs iterative web searches using Google Search and Gemini models to generate structured reports with citations. It integrates with MCP-compatible clients like Claude and Cursor to enable sophisticated, multi-step AI research workflows.
    Last updated
    MIT