"Elastic" matching MCP servers:

Small production-oriented MCP server for diagnosing incidents from Elasticsearch logs with unknown schema. It provides tools for log discovery, retrieval, and issue diagnosis.

Connects to Elasticsearch clusters through the Model Context Protocol, enabling natural language querying and management of Elasticsearch data. Provides tools to search indices, list available indices, and retrieve index mappings.

A Python MCP server that provides AI agents with a controlled, read-first interface to Elasticsearch, Kibana Security, Fleet, and Elastic Defend.

Connects LLMs to Elasticsearch with a Semantic-to-Lexical layer that translates technical field names into business knowledge, enabling autonomous querying without hardcoded domain logic.

Enables personal AI agents and users to reinforce habits, triage decisions, and prevent streak-break abandonment by generating adaptive micro-fallbacks and elastic recommendations.

A Python MCP server that enables semantic search through Search Labs blog posts indexed in Elasticsearch, allowing Claude to intelligently retrieve relevant information from the blog content.

Sigma detection rule writing, validation, and pySigma-based multi-backend conversion (Splunk, Elastic, Wazuh, Kibana) via 3 MCP tools and 3 Claude Code skills, backed by a 61-rule production corpus across 11 MITRE ATT\&CK tactic categories.

Enables AI assistants to search, analyze, and interact with Elasticsearch through natural language.

Enables any MCP-compatible agent to query, diagnose, map, and operate Elasticsearch or OpenSearch clusters via REST, with read-only defaults and layered safety controls.

Provides AI assistants with access to materials science databases, enabling search and analysis of material properties, crystal structures, phase diagrams, and elastic properties through the Materials Project API.

An MCP server that indexes PDF documentation and text into Elasticsearch for semantic search and retrieval. It enables users to query knowledge bases, ingest new files, and dynamically update content through MCP-compatible clients like Claude Desktop and Cursor.

An MCP server that enables interaction with Elasticsearch and OpenSearch clusters for searching documents and managing indices. It provides tools for cluster health monitoring, index configuration, and general API requests.

Facilitates interaction with Elasticsearch clusters by allowing users to perform index operations, document searches, and cluster management via a Model Context Protocol server and natural language commands.

Provides an MCP protocol interface for interacting with Elasticsearch 7.x databases, supporting comprehensive search functionality including aggregations, highlighting, and sorting.

This MCP server enables classification of personal inbox messages by actionability, effort duration, and urgency, producing prioritized queues and elastic recommendations that help prevent attention fragmentation.

Provides persistent, intelligent memory using Elasticsearch with hierarchical categorization and semantic search for LLM contexts.

Enables users and autonomous agents to calculate dynamic price elasticity of demand and identify marginal revenue maxima through a zero-dependency, MCP-compatible Python tool.

Transforms Elasticsearch into an AI-powered observability engine for analyzing logs, APM traces, and system metrics through natural language.

Exposes Elasticsearch semantic search capabilities as MCP tools, enabling ELSER, kNN, and hybrid search with automatic index setup.

Enables MCP-compatible AI clients to search and retrieve Elasticsearch application logs through a provided search tool.