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    Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.
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    An MCP server that provides AI agents with structured, real-time data on cryptocurrency investment funds, enabling deeper due diligence and portfolio intelligence.
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    MIT
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    A beginner-friendly Model Context Protocol (MCP) server that helps users understand MCP concepts, provides interactive examples, and lists available MCP servers. This server is designed to be a helpful companion for developers working with MCP. Also comes with a huge list of servers you can install.
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    Apache 2.0
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    Enables AI agents to access and manage project guidelines, documentation, and context through a structured content system with template support and workflow management.
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    MIT
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    Enables AI assistants to interact with self-hosted Dynatrace Managed environments to retrieve observability data, security insights, and performance metrics. It allows users to query problems, logs, events, and SLOs through natural language interfaces in both local and remote modes.
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    Apache 2.0
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    Enables academic research through paper search across multiple databases (IACR, CryptoBib, Crossref, Google Scholar), PDF processing, and GitHub repository browsing. Features modular architecture with FastMCP-based proxy server routing to specialized academic tools.
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    MIT
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    Enables AI agents to search through OpenShift service notification logs using semantic search powered by ChromaDB and sentence transformers, helping find relevant notifications based on problem descriptions with metadata enrichment and variable interpolation support.
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    Apache 2.0
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    Provides semantic search capabilities over the Plesk Extensions Guide documentation using Retrieval-Augmented Generation (RAG) and vector embeddings. It enables AI assistants to retrieve relevant technical information and answer natural language queries regarding Plesk extension development.
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    Enables access to Australian real estate data through the Realty In Au API, supporting property listings, agent/agency information, property details, school lookups, and property search with various filters.
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    MIT
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    Enables users to access the Realty In Ca1 API to search for residential and commercial property listings, view detailed property information, and retrieve neighborhood demographics or statistics. It supports filtering by geographic location, price range, building type, and other real estate characteristics.
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    Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.
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    MIT