Elasticsearch MCP Server

Name: Elasticsearch MCP Server
Author: MCP-Mirror

@MCP-Mirror

リポジトリ

基本情報

カテゴリ

データベース

トランスポート

stdio

作者

MCP-Mirror

Elasticsearch MCP Server について

Mirror of

#elasticsearch #mcp-server#llm-management

Elasticsearch MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "da1y_mcp-server-elasticsearch": {
      "command": "docker",
      "args": [
        "run",
        "-d",
        "--name",
        "elasticsearch",
        "-p",
        "9200:9200",
        "-p",
        "9300:9300",
        "-e",
        "discovery.type=single-node",
        "-e",
        "xpack.security.enabled=false",
        "docker.elastic.co/elasticsearch/elasticsearch:8.11.3"
      ]
    }
  }
}

Elasticsearch MCP Server のツール

Execute search queries against indices

Create new Elasticsearch indices

List all available indices

Index a document

概要

What is Elasticsearch?

This is a Model Context Protocol (MCP) server for Elasticsearch clusters. It enables LLMs to manage indices and execute search queries by exposing tools and index mapping resources.

How to use Elasticsearch?

Add the server to the mcpServers section of your claude_desktop_config.json using the command npx -y @modelcontextprotocol/server-elasticsearch <url> (e.g., http://localhost:9200). A local Elasticsearch instance can be started with the provided Docker one‑liner docker run -d --name elasticsearch -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" -e "xpack.security.enabled=false" docker.elastic.co/elasticsearch/elasticsearch:8.11.3.

Key features of Elasticsearch?

Execute search queries against indices using Elasticsearch Query DSL.
Create new indices with optional mappings and settings.
List all available indices.
Index documents with an optional custom ID.
Automatically discover index mappings as resources.

Use cases of Elasticsearch?

Let an AI assistant search an Elasticsearch index for relevant documents.
Automatically create new indices with proper mappings for application data.
List all indices in a cluster to understand existing data organization.
Index incoming documents

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