OpenSearch MCP Server Integration

What does “OpenSearch” MCP Server do?

The OpenSearch MCP (Model Context Protocol) Server acts as a bridge between AI assistants and the OpenSearch platform, enabling seamless integration and enhanced development workflows. By exposing OpenSearch data and functionality through the MCP protocol, this server allows AI clients to interact programmatically with OpenSearch indices, execute queries, retrieve documents, and manage search infrastructure. This empowers developers and AI agents to perform sophisticated data analysis, real-time search, and content management tasks, all from within their preferred AI or automation tools. The server is designed to streamline processes such as querying, data enrichment, and operational monitoring, making it an essential tool for anyone leveraging OpenSearch in AI-driven environments.

List of Prompts

(No prompt templates are mentioned in the provided repository content.)

List of Resources

(No explicit resource primitives are described in the available repository content.)

List of Tools

(Specific tools exposed by the server are not listed in the available documentation or code index.)

Use Cases of this MCP Server

• Search and Retrieval: AI agents can query OpenSearch indices to retrieve relevant documents or data, enhancing information retrieval for chatbots and virtual assistants.
• Data Analytics: Developers can leverage the server to perform complex analytics on large datasets stored in OpenSearch, automating insights generation.
• Content Management: Automated workflows can manage, index, and update documents in OpenSearch, streamlining content operations.
• Monitoring and Alerting: Use the server to monitor search cluster health and trigger alerts or actions based on real-time data.
• Integration with AI Workflows: Incorporate OpenSearch-powered search and analytics directly into AI-driven pipelines for smarter decision-making.

How to set it up

Windsurf

1. Ensure that Python is installed and the OpenSearch MCP server is available on your system.
2. Open your Windsurf configuration file (e.g., windsurf.json).
3. Add the OpenSearch MCP server under the mcpServers object with the appropriate command and arguments.
4. Save the configuration and restart Windsurf.
5. Verify the setup by checking the MCP server status in Windsurf.

Example JSON:

{
  "mcpServers": {
    "opensearch-mcp": {
      "command": "python",
      "args": ["-m", "opensearch_mcp_server"]
    }
  }
}

Claude

1. Install Python and ensure the OpenSearch MCP server is accessible.
2. Edit the Claude configuration file to include the MCP server.
3. Add the server command and arguments in the mcpServers section.
4. Save changes and restart Claude.
5. Confirm the server is running via the Claude interface.

Example JSON:

{
  "mcpServers": {
    "opensearch-mcp": {
      "command": "python",
      "args": ["-m", "opensearch_mcp_server"]
    }
  }
}

Cursor

1. Download and install Python and the OpenSearch MCP server.
2. Open the Cursor configuration file.
3. Insert the MCP server details under mcpServers.
4. Save the file and restart the Cursor application.
5. Check for successful integration in Cursor.

Example JSON:

{
  "mcpServers": {
    "opensearch-mcp": {
      "command": "python",
      "args": ["-m", "opensearch_mcp_server"]
    }
  }
}

Cline

1. Make sure Python and the OpenSearch MCP server are installed.
2. Edit the Cline configuration to register the server.
3. Add the MCP server in the mcpServers section with command and args.
4. Save and restart Cline.
5. Validate the server is active and accessible.

Example JSON:

{
  "mcpServers": {
    "opensearch-mcp": {
      "command": "python",
      "args": ["-m", "opensearch_mcp_server"]
    }
  }
}

Securing API Keys with Environment Variables

Set sensitive API keys or credentials using environment variables in your configuration, for example:

{
  "mcpServers": {
    "opensearch-mcp": {
      "env": {
        "OPENSEARCH_API_KEY": "your_api_key_here"
      },
      "inputs": {
        "index": "your_index_name"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "opensearch-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “opensearch-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the tables above, the OpenSearch MCP Server provides a clear overview and setup instructions, but lacks detail on prompts, resources, and tools. It does include guidance on securing API keys. Overall, it offers the basics for integration but is missing advanced MCP primitives or feature descriptions.

MCP Score

I would rate this MCP server a 3/10 for general MCP readiness: it has standard setup and licensing, but lacks detailed implementation of tools, prompts, or resources that are key to advanced MCP use and agentic behaviors.