What Elasticsearch versions are supported?

The Elasticsearch MCP Server supports Elasticsearch versions 7.16 and later.

What is Elasticsearch MCP Server?

It's a server application built on Spring AI MCP that simplifies Elasticsearch data processing and search by leveraging MCP tools and the official Elasticsearch Java client.

What are the main benefits of using this tool?

Key benefits include automated MCP tool registration, seamless Elasticsearch cluster integration, a scalable architecture for adding new tools, and AI-powered queryDSL for document searches.

How does the tool integrate with Elasticsearch?

It utilizes the official Elasticsearch Java client to interact with Elasticsearch clusters, providing MCP tools for various tasks like cluster health checks, index mapping, and document searching.

What kind of MCP tools are available?

It offers a range of MCP tools including retrieving cluster health, statistics, mappings, indices, aliases, shard allocation, and searching for documents using AI-generated queryDSL.

Elasticsearch

Name: Elasticsearch
Author: silbaram

bysilbaram

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Database Management

Data Science & ML

Analytics & Monitoring

Facilitates Elasticsearch data processing and search through Spring AI's Model Context Protocol.

The Elasticsearch Model Context Protocol (MCP) Server is a Spring AI MCP-based application that simplifies the creation of data processing workflows and enables efficient indexing and searching within an Elasticsearch cluster. It automatically registers tools defined with the `@Tool` annotation, allowing external clients to call these functions via standard JSON-RPC communication. With seamless Elasticsearch integration, it provides access to major Elasticsearch APIs, offering cluster health checks, index mapping lookups, document indexing, and searching as MCP tools. Its scalable architecture ensures flexible management and easy addition of new tools or external integrations.

Key Features

01Automatically registers and executes MCP tools defined with the `@Tool` annotation.

02Integrates with Elasticsearch clusters using the official Java client.

03Provides MCP tools for cluster health checks, index mapping lookups, document indexing, and searching.

04Offers a modular package structure for flexible client settings and Elasticsearch version management.

05Supports scalable architecture for easy addition of new tools and external integrations.

061 GitHub stars

Use Cases

01Centralized data processing with Elasticsearch.

02AI-driven data analysis and querying within Elasticsearch.

03Programmatic Elasticsearch cluster monitoring and management.