What is Opensearch Mcp Integration

opensearch-mcp-integration is a guide designed to help users integrate Amazon Q with OpenSearch MCP servers for natural language interactions.

Use cases

Use cases include enhancing customer service chatbots with natural language queries, improving data retrieval systems, and integrating search functionalities into applications.

How to use

To use opensearch-mcp-integration, follow the step-by-step instructions provided in the guide, which includes setting up a local OpenSearch instance using Docker, installing the OpenSearch MCP server, and configuring Amazon Q.

Key features

Key features include a detailed setup process for OpenSearch with Docker, integration with Amazon Q, and support for natural language processing capabilities.

Where to use

opensearch-mcp-integration can be used in fields such as natural language processing, data search and retrieval, and any application requiring advanced search functionalities.

OpenSearch MCP <> LLM Integration Guide

This guide provides step-by-step instructions for integrating Amazon Q & Anthropic Claude desktop with OpenSearch using MCP servers for natural language interactions.

Anthropic Claude

Amazon Q

Prerequisites

• Docker and Docker Compose installed
• Python 3.x installed
• pipx installed (pip install pipx)
• Amazon Q CLI
• Anthropic Claude Desktop

1. Setting up Local OpenSearch Instance

1. Create a new directory for your project:

   mkdir opensearch-mcp && cd opensearch-mcp
   

2. Create a docker-compose.yml file with the following content:

   version: '3'
   services:
     opensearch:
       image: opensearchproject/opensearch:latest
       environment:
         - discovery.type=single-node
         - bootstrap.memory_lock=true
         - "OPENSEARCH_JAVA_OPTS=-Xms512m -Xmx512m"
         - "OPENSEARCH_INITIAL_ADMIN_PASSWORD=myStrongPassword123!"
         - "plugins.security.ssl.http.enabled=true"
         - "plugins.security.ssl.transport.enabled=true"
         - "plugins.security.ssl.http.pemcert_filepath=certs/node.pem"
         - "plugins.security.ssl.http.pemkey_filepath=certs/node-key.pem"
         - "plugins.security.ssl.http.pemtrustedcas_filepath=certs/root-ca.pem"
         - "plugins.security.ssl.transport.pemcert_filepath=certs/node.pem"
         - "plugins.security.ssl.transport.pemkey_filepath=certs/node-key.pem"
         - "plugins.security.ssl.transport.pemtrustedcas_filepath=certs/root-ca.pem"
       ulimits:
         memlock:
           soft: -1
           hard: -1
         nofile:
           soft: 65536
           hard: 65536
       volumes:
         - opensearch-data:/usr/share/opensearch/data
         - ./certs:/usr/share/opensearch/config/certs
       ports:
         - 9200:9200
         - 9600:9600
       networks:
         - opensearch-net
   
     opensearch-dashboards:
       image: opensearchproject/opensearch-dashboards:latest
       environment:
         - 'OPENSEARCH_HOSTS=["https://opensearch:9200"]'
         - "OPENSEARCH_SSL_VERIFICATIONMODE=none"
         - "OPENSEARCH_SECURITY_ADMIN_PASSWORD=myStrongPassword123!"
         - "SERVER_SSL_ENABLED=true"
         - "SERVER_SSL_CERTIFICATE=/usr/share/opensearch-dashboards/config/certs/node.pem"
         - "SERVER_SSL_KEY=/usr/share/opensearch-dashboards/config/certs/node-key.pem"
       ports:
         - 5601:5601
       volumes:
         - ./certs:/usr/share/opensearch-dashboards/config/certs
       depends_on:
         - opensearch
       networks:
         - opensearch-net
   
   volumes:
     opensearch-data:
   
   networks:
     opensearch-net:
   

3. Start the containers:

   docker compose up -d
   

2. Installing OpenSearch MCP Server

1. Install the OpenSearch MCP server using pipx:

   pipx install opensearch-mcp-server
   

3. Configuring LLMs with MCP server configurations

1. Amazon Q: Create a mcp.json file in your Amazon Q configuration directory (~/.aws/amazonq/mcp.json)

2. Anthropic Claude: Create a claude_desktop_config.json file in your Claude Desktop configuration directory (~/Library/Application Support/Claude/claude_desktop_config.json)

   {
     "mcpServers": {
       "opensearch-mcp-server": {
         "command": "/Users/YOUR_USERNAME/.local/bin/opensearch-mcp-server",
         "args": [],
         "env": {
           "OPENSEARCH_URL": "https://localhost:9200",
           "OPENSEARCH_SSL_VERIFY": "none",
           "OPENSEARCH_USERNAME": "admin",
           "OPENSEARCH_PASSWORD": "myStrongPassword123!"
         }
       }
     }
   }
   

   Replace YOUR_USERNAME with your system username or specify the location where the opensearch-mcp-server is installed.

4. Verifying the Setup

1. Check OpenSearch is running:

   curl -XGET -u admin:myStrongPassword123! https://localhost:9200
   

2. Verify OpenSearch Dashboards:

   • Open https://localhost:5601 in your browser

3. Check indices:

   curl -XGET -u admin:myStrongPassword123! https://localhost:9200/_cat/indices
   

5. Using Natural Language with OpenSearch

Here are some example commands you can try in LLMs:

1. Create an index:

   Create a new index called "books" with title and author fields
   

2. Add documents:

   Add a document to the books index with title "The Great Gatsby" and author "F. Scott Fitzgerald"
   

3. Search documents:

   Search for books by F. Scott Fitzgerald
   

4. Get index information:

   Show me the mapping of the books index
   

Troubleshooting

1. If MCP server fails to initialize:

   • Check if OpenSearch is running (docker ps)
   • Verify credentials in mcp_config.json
   • Check OpenSearch logs (docker compose logs opensearch)

2. If tools are not available:

   • Restart the LLMs
   • Check MCP server logs
   • Verify the mcp_config.json syntax

Available MCP Tools

The OpenSearch MCP server provides several tools for interacting with OpenSearch:

• list_indices: List all indices.
• get_index: Returns information (mappings, settings, aliases) about one or more indices. Args: index: Name of the index
• create_index: Create a new index. Args: index: Name of the index body: Optional index configuration including mappings and settings
• delete_index: Delete an index. Args: index: Name of the index
• search_documents: Search for documents. Args: index: Name of the index body: Search query
• index_document: Creates or updates a document in the index. Args: index: Name of the index document: Document data id: Optional document ID
• get_document: Get a document by ID. Args: index: Name of the index id: Document ID
• delete_document: Delete a document by ID. Args: index: Name of the index id: Document ID
• delete_by_query: Deletes documents matching the provided query. Args: index: Name of the index body: Query to match documents for deletion
• get_cluster_health: Returns basic information about the health of the cluster.
• get_cluster_stats: Returns high-level overview of cluster statistics.
• list_aliases: List all aliases.
• get_alias: Get alias information for a specific index. Args: index: Name of the index
• put_alias: Create or update an alias for a specific index. Args: index: Name of the index name: Name of the alias body: Alias configuration
• delete_alias: Delete an alias for a specific index. Args: index: Name of the index name: Name of the alias
• general_api_request: Perform a general HTTP API request. Use this tool for any Elasticsearch/OpenSearch API that does not have a dedicated tool. Args: method: HTTP method (GET, POST, PUT, DELETE, etc.) path: API endpoint path params: Query parameters body: Request body

Security Notes

1. Always use strong passwords in production
2. Enable SSL in production environments
3. Regularly rotate credentials

Additional Resources

• OpenSearch Documentation
• Amazon Q Documentation
• MCP Protocol Documentation