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"RAG (Retrieval-Augmented Generation) system that can store papers and web pages" matching MCP servers:

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    Enhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.
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    MIT
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    Enables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.
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    Apache 2.0
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    MCP server for a modular RAG system that enables natural language question answering over enterprise documents with intent-aware routing, adaptive retrieval, and citation-backed responses.
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    A black-box flight recorder for RAG retrieval inside MCP agents. Logs what chunks the model saw, scores, sources, and rankings - so you can audit, replay, and diff retrieval runs after the fact.
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    Enables LLMs to interact with vehicle CAN bus and OBD-II data through a simulated ECU environment. Provides tools for reading frames, decoding messages via DBC files, monitoring signals, and querying automotive diagnostics without requiring physical hardware.
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    MIT
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    Enables AI applications to access and contextualize organizational knowledge sources including GitHub repositories and internal documentation through standardized MCP protocol integration. Features OAuth 2.1 authentication, vector-based semantic search, and optimized context chunking for enterprise development workflows.
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    An MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.
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    MIT
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    Single MCP server that manages multiple personal collections (todos, bookmarks, etc.) with only six generic tools, driven by a YAML registry for validation.
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