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    Enables GitHub Copilot to query local ChromaDB instances to retrieve relevant documents and context for AI conversations. It allows users to search vector collections using natural language tools directly within VS Code.
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    An MCP server that enables semantic search over local files or GitHub repositories by indexing content into a serverless vector database, allowing AI assistants to understand meaning rather than just keywords.
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    MIT
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    Enables AI assistants to search and retrieve information from Teleport documentation using a vector database. It provides a tool for semantic vector search over pre-populated embeddings of Teleport pages and examples to assist with technical queries.
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    MIT
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    A visual Retrieval-Augmented Generation MCP server that renders web pages, PDFs, and design screenshots into visual tiles and enables natural language querying over them via multimodal vision embeddings.
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    Enables AI agents to semantically search GitHub repository documentation by automatically fetching, vectorizing, and indexing content into an Upstash Vector database. It provides a standard MCP interface for agents to retrieve relevant documentation snippets through natural language queries.
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    Enables hybrid search (dense + sparse) over self-hosted indexes of continuously ingested public datasets (news, GitHub, Wikipedia, arXiv, small web, devdocs, Hacker News) using your own embedding model, served via the Model Context Protocol.
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    Enables context-aware semantic search across codebases using Qdrant vector database with intelligent GitHub issue resolution, Projects V2 management, and progressive context retrieval for 95%+ token reduction in AI-assisted development.
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