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"Exploring GraphRAG and HybridRAG" matching MCP servers:

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    An MCP server that enables LLMs to perform semantic and fulltext searches within Neo4j while executing complex, search-augmented Cypher queries for GraphRAG applications. It provides tools for database schema discovery and supports multi-provider embeddings to facilitate advanced graph traversals.
    5
    2
    MIT
  • F
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    Enables AI agents to query a local knowledge graph built from document collections using hybrid search (BM25 + vector fusion) and entity-relationship extraction. Supports privacy-first, offline operation with tools for semantic search, entity graph exploration, and corpus statistics.
    3
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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
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    A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.
    Apache 2.0
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    A high-performance FastAPI server supporting Model Context Protocol (MCP) for seamless integration with Large Language Models, featuring REST, GraphQL, and WebSocket APIs, along with real-time monitoring and vector search capabilities.
    8
    MIT
  • A
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    A Python server that enables retrieval-augmented generation through semantic, question/answer, and style search modalities using PostgreSQL and pgvector for embedding storage and retrieval.
    2
    Apache 2.0
  • 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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    MCP server for Qdrant vector database with local BERT embeddings. Enables semantic search and vector storage operations through natural language.
    MIT
  • A
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    Enables interaction with DataStax Astra DB through the Model Context Protocol. Provides database connectivity and operations for Astra DB instances via secure token-based authentication.
    1
    MIT
  • A
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    MCP server for Milvus vector database enabling vector search, text search, and hybrid search operations.
    Apache 2.0
  • A
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    Provides a comprehensive Model Context Protocol interface for RAGFlow, enabling AI models to perform semantic retrieval, manage datasets, and handle document chunks. It supports advanced features like GraphRAG and RAPTOR for sophisticated knowledge base management and natural language querying.
    MIT
  • A
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    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
  • A
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    Enables querying a hybrid system that combines Neo4j graph database and Qdrant vector database for powerful semantic and graph-based document retrieval through the Model Context Protocol.
    63
    MIT
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    An MCP server that enables AI assistants to directly interact with Elasticsearch for searching, aggregating, and retrieving documents from indices, supporting full-text search, semantic search, and various query modes.
    15
    MIT
  • A
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    Enables document ingestion, semantic search, and retrieval-augmented generation via MCP tools and REST API, using vector embeddings and intelligent chunking.
    MIT