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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.
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    MIT
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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.
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    MIT
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    The MCP Server for Weaviate facilitates integration with Weaviate using a customizable Python-based server, enabling interaction with Weaviate databases and OpenAI APIs via configurable URL and API keys.
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    162
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    A Python server that enables AI assistants to perform hybrid search queries against Apache Solr indexes through the Model Context Protocol, combining keyword precision with vector-based semantic understanding.
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    MIT
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    Enables AI agents to interact with TigerGraph databases through the Model Context Protocol, supporting graph operations, schema queries, and GSQL execution via natural language.
    Last updated
    Apache 2.0
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    A Docker-based local RAG backend that provides advanced document search capabilities using vector, graph, and full-text retrieval via the Model Context Protocol. It supports over 28 file formats and tracks evolving relationships between concepts using a Neo4j-backed graphiti implementation.
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    1
    MIT
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    A simple Model Context Protocol (MCP) server with ChromaDB integration, allowing AI assistants to interact with ChromaDB for vector storage and retrieval operations.
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    GPL 3.0
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    Enables semantic code search across codebases using Qdrant vector database and OpenAI embeddings, allowing users to find code by meaning rather than just keywords through natural language queries.
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    MIT
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    Multi-modal RAG service that exposes both REST API and MCP server for document indexing and knowledge-base querying, supporting graph-based LightRAG and classical RAG pipelines with file retrieval from MinIO and PostgreSQL-backed knowledge graph.
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    14
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    Enables semantic search through a Pinecone vector database containing economic books and academic papers using natural language queries. Provides 10 specialized search tools with metadata filtering for precise discovery of economic theories, concepts, and research by author, subject, or book.
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    A minimalist indexing tool that provides AI agents with semantic search and structural AST parsing for deep codebase understanding. It enables autonomous agents to navigate large codebases predictably using vector embeddings and native language server capabilities like definition and reference tracking.
    Last updated