opensearch-mcp 0.0.1

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OpenSearch MCP Server

A minimal Model Context Protocol (MCP) server for OpenSearch exposing 4 tools over stdio and sse server.

Available tools

• ListIndexTool: Lists all indices in OpenSearch.
• IndexMappingTool: Retrieves index mapping and setting information for an index in OpenSearch.
• SearchIndexTool: Searches an index using a query written in query domain-specific language (DSL) in OpenSearch.
• GetShardsTool: Gets information about shards in OpenSearch.

More tools coming soon. Click here

User Guide

Installation

Install from PyPI:

pip install test-opensearch-mcp

Configuration

Authentication Methods:

• Basic Authentication

export OPENSEARCH_URL="<your_opensearch_domain_url>"
export OPENSEARCH_USERNAME="<your_opensearch_domain_username>"
export OPENSEARCH_PASSWORD="<your_opensearch_domain_password>"

• IAM Role Authentication

export OPENSEARCH_URL="<your_opensearch_domain_url>"
export AWS_REGION="<your_aws_region>"
export AWS_ACCESS_KEY="<your_aws_access_key>"
export AWS_SECRET_ACCESS_KEY="<your_aws_secret_access_key>"
export AWS_SESSION_TOKEN="<your_aws_session_token>"

Running the Server

# Stdio Server
python -m mcp_server_opensearch

# SSE Server
python -m mcp_server_opensearch --transport sse

Claude Desktop Integration

• Using the Published PyPI Package (Recommended)

{
    "mcpServers": {
        "opensearch-mcp-server": {
            "command": "uvx",
            "args": [
                "test-opensearch-mcp"
            ],
            "env": {
                // Required
                "OPENSEARCH_URL": "<your_opensearch_domain_url>",

                // For Basic Authentication
                "OPENSEARCH_USERNAME": "<your_opensearch_domain_username>",
                "OPENSEARCH_PASSWORD": "<your_opensearch_domain_password>",

                // For IAM Role Authentication
                "AWS_REGION": "<your_aws_region>",
                "AWS_ACCESS_KEY": "<your_aws_access_key>",
                "AWS_SECRET_ACCESS_KEY": "<your_aws_secret_access_key>",
                "AWS_SESSION_TOKEN": "<your_aws_session_token>"
            }
        }
    }
}

• Using the Installed Package (via pip):

{
    "mcpServers": {
        "opensearch-mcp-server": {
            "command": "python",  // Or full path to python with PyPI package installed
            "args": [
                "-m",
                "mcp_server_opensearch"
            ],
            "env": {
                // Required
                "OPENSEARCH_URL": "<your_opensearch_domain_url>",

                // For Basic Authentication
                "OPENSEARCH_USERNAME": "<your_opensearch_domain_username>",
                "OPENSEARCH_PASSWORD": "<your_opensearch_domain_password>",

                // For IAM Role Authentication
                "AWS_REGION": "<your_aws_region>",
                "AWS_ACCESS_KEY": "<your_aws_access_key>",
                "AWS_SECRET_ACCESS_KEY": "<your_aws_secret_access_key>",
                "AWS_SESSION_TOKEN": "<your_aws_session_token>"
            }
        }
    }
}

LangChain Integration

The OpenSearch MCP server can be easily integrated with LangChain using the SSE server transport

Prerequisites

1. Install required packages

pip install langchain langchain-mcp-adapters langchain-openai

2. Set up OpenAI API key

export OPENAI_API_KEY="<your-openai-key>"

3. Ensure OpenSearch MCP server is running in SSE mode

python -m mcp_server_opensearch --transport sse

Example Integration Script

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langchain.agents import AgentType, initialize_agent

# Initialize LLM (can use any LangChain-compatible LLM)
model = ChatOpenAI(model="gpt-4o")

async def main():
    # Connect to MCP server and create agent
    async with MultiServerMCPClient({
        "opensearch-mcp-server": {
            "transport": "sse",
            "url": "http://localhost:9900/sse",  # SSE server endpoint
            "headers": {
                "Authorization": "Bearer secret-token",
            }
        }
    }) as client:
        tools = client.get_tools()
        agent = initialize_agent(
            tools=tools,
            llm=model,
            agent=AgentType.OPENAI_FUNCTIONS,
            verbose=True,  # Enables detailed output of the agent's thought process
        )

        # Example query
        await agent.ainvoke({"input": "List all indices"})

if __name__ == "__main__":
    asyncio.run(main())

Notes:

• The script is compatible with any LLM that integrates with LangChain and supports tool calling
• Make sure the OpenSearch MCP server is running before executing the script
• Configure authentication and environment variables as needed

Development

Interested in contributing? Check out our:

• Development Guide - Setup your development environment
• Contributing Guidelines - Learn how to contribute