This Elasticsearch MCP server enables AI models to interact with Elasticsearch clusters, providing tools for managing indices and executing queries. Developed as an open-source project, it integrates with the @elastic/elasticsearch library to offer functionalities like searching, creating indices, listing indices, and indexing documents. The server is built with TypeScript and leverages the @modelcontextprotocol/sdk for MCP implementation. By abstracting Elasticsearch operations, it allows AI systems to easily incorporate powerful search and analytics capabilities into their workflows. This implementation is particularly useful for developers and data scientists working with large datasets, enabling use cases like intelligent data retrieval, automated index management, and AI-driven data analysis in Elasticsearch environments.
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Execute search queries against indices. Input: index (string): Target index name, query (object): Elasticsearch query DSL. Returns search hits.
Create new Elasticsearch indices. Input: index (string): Index name, mappings (object, optional): Index mappings configuration, settings (object, optional): Index settings configuration.
List all available indices. No input required. Returns array of index information.
Index a document. Input: index (string): Target index name, id (string, optional): Document ID, document (object): Document content. Returns indexing operation result.
