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    An MCP service for AI-assisted reasoning and editing on long-form fiction projects. It builds a structured index from scene files for targeted context retrieval.
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    AGPL 3.0
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    Improves AI agent task performance by providing task area segmentation, hierarchical RAG, and hybrid search capabilities.
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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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    MCP server that integrates a 1200-paper RAG database with six tools to support research workflows across stages like hypothesis, experiment, statistics, and writing. It routes requests to specialized skills and real-time frontier searches to provide evidence-grounded research mentoring.
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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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    MCP server that answers questions using Reddit discussions as evidence, searching threads, fetching comments, and ranking useful context, with optional cited answer synthesis.
    MIT
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    Provides a private set of high-level tools for web research, focused URL investigation, read-only GitHub research, and persistent semantic memory via a Streamable HTTP MCP gateway.
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    Enables semantic search across personal PDF paper collections with page-level citations, allowing users to query their library from any MCP-capable client.
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    An open-source deep research MCP server that provides multi-source web search and synthesis with citations, enabling agents to perform citation-backed research using Qwen3-30B-A3B-Thinking and other models.
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    MIT
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    Enables AI assistants to perform comprehensive research by searching Google, mining Reddit discussions, scraping web content with JS rendering, and synthesizing findings with citations into structured context.
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    Provides specialized AI-powered comprehensive research and analysis capabilities by integrating with advanced deep research agents, offering unlimited queries with no rate limits and faster performance than comparable services.
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    MIT
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    Provides structured web search capabilities optimized for technical and software development content via providers like OpenRouter. It enables AI agents to perform research and retrieve relevant technical data in a consistent, programmatic JSON format.
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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 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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