Elasticsearch provides MCP server capabilities enabling LLMs and other clients to interact with Elasticsearch clusters for managing indices and executing search queries using natural language, demonstrating a powerful MCP server integration. Several MCP server implementations exist to facilitate semantic and natural language search with Elasticsearch, including cr7258/elasticsearch-mcp-server, elastic-semantic-search-server, Elasticsearch Mcp, Elasticsearch Server, and Elasticsearch7 Server.
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MCP server implementation for Alibaba Cloud Tablestore, supporting document management, semantic and vector search, RAG-friendly, and serverless capabilities.
An MCP server bridging to ClickZetta databases for executing SQL queries, exploring schema information, and documenting insights via natural language interaction.
A Model Context Protocol server for MongoDB, enabling LLMs to query, insert, delete, and aggregate documents via natural language—demonstrating MCP server database integration.
A Pinecone MCP server providing vector search capabilities over Pinecone through the Model Context Protocol.
MCP server for managing Redis Cloud resources using natural language, supporting database creation, subscription monitoring, and deployment configuration.
A purpose-built MCP server that enables semantic data retrieval via vector embeddings, allowing AI systems to perform meaning-based searches in large datasets. Qdrant is a leading example, offering standard protocols for vector operations.
Elasticsearch is a free and open source, distributed, RESTful search and analytics engine. It is designed for scalability, speed, and relevance, and can be used as a vector database optimized for production-scale workloads. Elasticsearch is the core of Elastic’s open Stack platform and is suitable for a wide range of use cases including full-text search, vector search, analytics, and more.