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"A tool for finding academic papers using semantic search and citation analysis" matching MCP servers:

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    Enables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.
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    BSD 2-Clause "Simplified"
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    Enables real-time search and retrieval of academic paper information from multiple sources, providing access to paper metadata, abstracts, and full-text content when available, with structured data responses for integration with AI models that support tool/function calling.
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    Enables AI agents to perform semantic search over codebases by converting natural language queries into efficient search patterns like grep and ripgrep. It utilizes LLMs to verify relevance and find code snippets that traditional keyword-based searches might miss.
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    Local offline semantic search over documents (txt, md, pdf, docx, pptx, csv). Indexes folders into a LanceDB vector database with multilingual embeddings and supports hybrid vector + keyword search via Reciprocal Rank Fusion. No API keys, no cloud, no Docker required.
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    AGPL 3.0
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    Enables AI tools to search and retrieve data from various South Korean and Japanese academic databases, including research journals, historical records, and legislative information.
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    An unofficial MCP server that provides semantic search capabilities for Hugging Face models and datasets, enabling Claude and other MCP-compatible clients to search, discover, and explore the Hugging Face ecosystem using natural language queries.
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    MIT
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    Enables AI assistants to search across multiple academic databases (PubMed, arXiv, bioRxiv, medRxiv, Semantic Scholar) through a unified interface. Supports advanced filtering, metadata retrieval, PDF downloads, and comprehensive research workflows with citation analysis.
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    Provides tools for AI models to search and retrieve information from five major Polish academic and government databases, including scientific articles and research datasets. It enables users to query platforms like Biblioteka Nauki and Repozytorium UJ through a standardized interface hosted on Cloudflare Workers.
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    MIT
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    Reduces token consumption by over 80% through intelligent file caching, returning only diffs for modified files and suppressing unchanged content. It features a suite of 12 tools for semantic search, batch reading, and efficient file editing to optimize LLM interactions with large codebases.
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    MIT
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    A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.
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    Enables AI agents to perform semantic code search across entire codebases using natural language queries. Provides fast indexing and ranked search results with line numbers and file paths through the Seroost search engine.
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