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"Automating Design and Deployment of n8n Workflows" matching MCP servers:

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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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    A
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    Provides semantic search capabilities by connecting Claude Desktop to a Cloudflare Workers backend powered by Vectorize. It enables natural language querying of knowledge bases using vector similarity and edge-based embedding generation.
    2
    MIT
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    Connects AI clients to MindsDB via the MySQL protocol to execute SQL queries, manage databases, and perform semantic searches within knowledge bases. It enables automated workflows through job scheduling and provides seamless integration with external data sources.
    11
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    A Node.js-based MCP server that enables AI agents to generate embeddings, index documents, and perform semantic vector searches using OpenAI and Chroma. It facilitates the creation of retrieval-augmented generation (RAG) pipelines for internal knowledge assistants and document-based workflows.
    3
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    A service discovery and proxy for MCP servers that enables registration, discovery, and execution of tools on remote MCP servers. Uses vector-based similarity search through Alibaba Cloud services to intelligently route requests to appropriate MCP services.
    3
  • -
    license
    C
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    A lightweight server implementation of the Model Context Protocol that connects Memgraph database with LLMs, allowing users to interact with graph databases through natural language.
    1
    25
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    Persistent memory MCP server for Claude Code — self-hosted, n8n + PostgreSQL + pgvector. Team memory for AI agents with multi-user roles, multi-project namespacing, and hybrid vector + keyword search. No cloud required.
    9
    MIT
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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.
    189
    MIT
  • A
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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
  • A
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    quality
    B
    maintenance
    Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.
    MIT
  • A
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    Not graded
    quality
    D
    maintenance
    A lightweight server that provides persistent memory and context management for AI assistants using local vector storage and database, enabling efficient storage and retrieval of contextual information through semantic search and indexed retrieval.
    2
    MIT
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    maintenance
    MCP server for Qdrant vector database with local BERT embeddings. Enables semantic search and vector storage operations through natural language.
    MIT
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    quality
    D
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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.
    17
    AGPL 3.0
  • A
    license
    Not graded
    quality
    D
    maintenance
    A local knowledge base system based on ChromaDB that supports automatic chunking, vector storage, and efficient similarity retrieval of txt and pdf documents, with MCP protocol support allowing AI assistants to directly access knowledge management functions.
    MIT
  • A
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    Not graded
    quality
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    maintenance
    Enables AI assistants to interact with MariaDB databases through standard SQL operations and advanced vector/embedding-based search. Supports database management, schema inspection, and semantic document storage and retrieval with multiple embedding providers.
    MIT