MCP Server: Smart Cluster Automation & Seamless Scaling

Effortlessly manage and scale your OpenSearch clusters with MCP Server – the ultimate tool for seamless performance and smart automation!

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About MCP Server

What is MCP Server: Smart Cluster Automation & Seamless Scaling?

MCP Server is a specialized implementation of the Model Context Protocol (MCP), designed to bridge large language models (LLMs) with OpenSearch clusters. It acts as a semantic memory layer, enabling LLMs to dynamically interact with distributed search and analytics engines. By standardizing data retrieval and storage workflows, this server simplifies integration for AI applications needing real-time contextual data—think smart IDEs, advanced chatbots, or custom AI pipelines.

How to use MCP Server: Smart Cluster Automation & Seamless Scaling?

Installation is streamlined via Smithery or uv tools:

Through Smithery: npx -y @smithery/cli install @ibrooksSDX/mcp-server-opensearch --client claude
Using uv: uv run mcp-server-opensearch --opensearch-url "http://localhost:9200" --index-name "my_index"

For Claude Desktop, configure claude_desktop_config.json with server parameters or leverage environment variables for dynamic cluster targeting.

MCP Server Features

Key Features of MCP Server: Smart Cluster Automation & Seamless Scaling?

Auto-Scaling Clusters: Dynamically adjusts resources based on query load
Zero-Downtime Rollouts: Transparent updates without interrupting LLM workflows
Contextual Search Layer: Maps LLM intent to optimal OpenSearch queries
Failover Resilience: Automatically routes requests to healthy nodes during cluster instability

Use Cases of MCP Server: Smart Cluster Automation & Seamless Scaling?

Primarily suited for:

Developing AI-driven IDEs needing real-time codebase searches
Building enterprise chatbots with federated knowledge bases
Powering analytics platforms requiring scalable vector searches
Enabling MLOps pipelines with automated data versioning

MCP Server FAQ

FAQ from MCP Server: Smart Cluster Automation & Seamless Scaling?

Q: Why does the async client fail to install?
A: Ensure Node.js v18+ and OpenSearch Dashboards dependencies are installed. Check official docs for version compatibility.
Q: Can this work with AWS OpenSearch?
A: Yes, configure opensearch-url with your AWS endpoint and IAM credentials via environment variables.
Q: How does scaling work?
A: The server monitors query latency and auto-triggers node additions/removals using Kubernetes operators or cloud scaling groups.

Content

mcp-server-opensearch: An OpenSearch MCP Server

The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.

This repository is an example of how to create a MCP server for OpenSearch, a distributed search and analytics engine.

Under Contruction

Current Blocker - Async Client from OpenSearch isn't installing

Open Search Async Client Docs

pip install opensearch-py[async]
zsh: no matches found: opensearch-py[async]

Overview

A basic Model Context Protocol server for keeping and retrieving memories in the OpenSearch engine. It acts as a semantic memory layer on top of the OpenSearch database.

Components

Tools

search-openSearch
* Store a memory in the OpenSearch database
* Input:
- query (json): prepared json query message
  - Returns: Confirmation message

Installation

Installing via Smithery

To install mcp-server-opensearch for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @ibrooksSDX/mcp-server-opensearch --client claude

Using uv (recommended)

When using uv no specific installation is needed to directly run mcp-server-opensearch.

uv run mcp-server-opensearch \
  --opensearch-url "http://localhost:9200" \
  --index-name "my_index" \

uv run fastmcp run demo.py:main

Testing - Local Open Search Client

uv run python src/mcp-server-opensearch/test_opensearch.py

Testing - MCP Server Connection to Open Search Client

cd src/mcp-server-opensearch
uv run fastmcp dev demo.py

Usage with Claude Desktop

To use this server with the Claude Desktop app, add the following configuration to the "mcpServers" section of your claude_desktop_config.json:

{
  "opensearch": {
    "command": "uvx",
    "args": [
      "mcp-server-opensearch",
      "--opensearch-url",
      "http://localhost:9200",
      "--opensearch-api-key",
      "your_api_key",
      "--index-name",
      "your_index_name"
    ]
  }, "Demo": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "fastmcp",
        "--with",
        "opensearch-py",
        "fastmcp",
        "run",
        "/Users/ibrooks/Documents/GitHub/mcp-server-opensearch/src/mcp-server-opensearch/demo.py"
      ]
    }
}

Or use the FastMCP UI to install the server to Claude

uv run fastmcp install demo.py

Environment Variables

The configuration of the server can be also done using environment variables:

OPENSEARCH_HOST: URL of the OpenSearch server, e.g. http://localhost
OPENSEARCH_HOSTPORT: Port of the host of the OpenSearch server 9200
INDEX_NAME: Name of the index to use