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"An overview of Supabase" matching MCP servers:

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    An MCP server that enables RAG-powered AI chat integration for websites by crawling content, building local vector stores, and generating embeddable chat widgets. It simplifies the setup of local chat servers with support for various LLM and embedding providers.
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    MIT
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    MCP server for Vectros, a typed multi-tenant record store with hybrid search and citation-grounded RAG, enabling agents to query, search, and ask questions over their own indexed data.
    80
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    Apache 2.0
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    An interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.
    188
    MIT
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    An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.
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    MIT
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    A server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.
    12
    MIT
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    Wraps n8n with an MCP server and vector storage to enable semantic search, management, and execution of automated workflows. It integrates with other tools to make workflows searchable and orchestratable within a larger automation ecosystem.
    1
    MIT
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    An MCP server that provides persistent semantic memory backed by PostgreSQL and pgvector for storing and searching thoughts via vector embeddings. It enables dimensional organization, conflict detection, and historical tracking of facts, decisions, and observations.
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    AGPL 3.0
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    An intelligent codebase processing server that provides agentic RAG capabilities for code repositories, enabling semantic search and contextual understanding through self-evaluating retrieval loops.
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    MIT
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    Exposes LangChain and Anthropic Claude capabilities as tools for generating production-ready RAG systems, Supabase vector stores, and document ingestion pipelines. It enables users to instantly scaffold AI infrastructure and document processing code through natural language prompts in MCP-compatible clients.