The Elasticsearch MCP Server project enables natural - language interactive queries of Elasticsearch data through the Model Context Protocol (MCP).

An implementation of the MCP Server for Elasticsearch/OpenSearch, providing document search, index analysis, and cluster management functions.

The Kibana MCP Server project is a community-maintained tool that allows MCP-compatible clients (such as Claude Desktop) to access Kibana instances through natural language or programming. It is based on the official API documentation of Elastic Kibana, providing functions such as security authentication, API endpoint management, and search execution, and supporting two interaction modes: tools and resources.

This project implements an MCP server for Elasticsearch, providing tools and resources for interacting with the Elasticsearch cluster, including functions such as index listing, mapping retrieval, and search, and supporting access to Elasticsearch data through the MCP protocol.

Agent Knowledge MCP is a comprehensive knowledge management solution that integrates Elasticsearch search, file operations, document validation, and version control functions, providing powerful knowledge management capabilities for AI assistants.

Agent Knowledge MCP is a comprehensive knowledge management solution integrating Elasticsearch search, file operations, document validation, and version control functions, providing powerful knowledge management capabilities for AI assistants.

A model context protocol server designed for Elasticsearch clusters, supporting LLM to manage indices and perform query operations

This is a service that provides an MCP protocol interface for Elasticsearch 7.x, supporting basic Elasticsearch operations and complete search functions, including advanced features such as aggregation queries and highlighting.

MCP Memory is a persistent memory system based on Elasticsearch, providing long - term memory storage capabilities for AI conversations and solving the problem of context loss in traditional AI conversations.

The Elasticsearch MCP Server is a service that connects to an Elasticsearch cluster via the Model Context Protocol (MCP), supporting natural language interaction to operate on ES data.

The Apple Health MCP Server is a model context protocol server based on the FastMCP framework, designed for LLM agents to interact with Apple Health data. It provides a standardized interface to index and query health data through Elasticsearch, supports natural - language search, analysis, and statistical functions, includes two major toolkits for XML and Elasticsearch, and supports Docker deployment.

This project is a semantic search tool based on Elasticsearch for semantic retrieval of Search Labs blog posts, including crawler configuration, index mapping updates, and MCP server integration functions.

The Octodet Elasticsearch MCP Server is an Elasticsearch operation service based on the Model Context Protocol (MCP). It provides a complete set of tools for interacting with Elasticsearch clusters for LLM applications, supporting functions such as document CRUD, batch operations, query update/deletion, cluster management, and advanced search.

A production - grade open - source MCP server for integrating Wazuh security data with LLMs (such as the Claude desktop application). This service authenticates through the Wazuh RESTful API, retrieves alerts from the Elasticsearch index, converts events into an MCP - compatible JSON format, and exposes an HTTP endpoint for the Claude desktop to obtain real - time security context.

A backend server integrated with Elasticsearch for managing MCP (Message Conversion Protocol) data, providing RESTful API interfaces and efficient data search and indexing functions.

The Elasticsearch MCP Server is an experimental project that allows clients to directly connect to Elasticsearch data via the Model Context Protocol (MCP), supporting natural language interaction to query indexes, mappings, and search data.

A model context protocol server designed for Elasticsearch clusters, supporting LLMs to manage indices and execute query operations.

Implementation of the Elasticsearch MCP server, providing index operations, document search, and cluster management functions, supporting interaction with Elasticsearch through natural language.

This project is the Kubernetes deployment configuration for the Model Context Protocol (MCP) server, including Docker image building, pushing to the Azure Container Registry, and K8s cluster deployment processes, supporting health checks, security protection, and elastic scaling.

An Elasticsearch MCP server implemented in Python, which requires the support of the Claude desktop environment and an Elasticsearch instance

A Python - based MCP server for controlling Elastica soft slender body physical simulations through natural language

MCP Memory is a persistent memory system based on Elasticsearch, providing cross - session memory storage and retrieval functions for AI conversations, solving the AI forgetting problem and enabling long - term context association.

This project is a semantic search tool based on Elasticsearch for semantic retrieval of Search Labs blog posts, including crawler configuration, index mapping update, and MCP server integration functions.

The MCP protocol server for Elasticsearch 7.x, providing a compatible interface with the Elasticsearch 7.x version and supporting basic operations and advanced search features.

This project demonstrates how to transform the official TypeScript MCP server into an implementation that supports the streamable HTTP protocol and is deployed via Amazon Lambda. Core features include support for the streaming protocol, elastic deployment based on Lambda, and cost optimization, suitable for scenarios that require seamless integration with existing HTTP infrastructure.