mcp-server-opensearch: An OpenSearch MCP Server

@MCP-Mirror

代码仓库

基本信息

分类

数据库

许可证

Apache-2.0 license

传输方式

stdio

作者

MCP-Mirror

关于 mcp-server-opensearch: An OpenSearch MCP Server

Mirror of

#mcp-server #opensearch #model-context-protocol

mcp-server-opensearch: An OpenSearch MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "ibrooksSDX_mcp-server-opensearch": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli",
        "install",
        "@ibrooksSDX/mcp-server-opensearch",
        "--client",
        "claude"
      ]
    }
  }
}

mcp-server-opensearch: An OpenSearch MCP Server 工具

`query` (json): prepared json query message

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概览

What is mcp-server-opensearch?

mcp-server-opensearch is a basic Model Context Protocol (MCP) server that acts as a semantic memory layer on top of the OpenSearch distributed search and analytics engine. It is designed for developers who need to integrate LLM applications with OpenSearch for storing and retrieving memories.

How to use mcp-server-opensearch?

Install automatically via Smithery (npx -y @smithery/cli install @ibrooksSDX/mcp-server-opensearch --client claude) or run directly with uv run mcp-server-opensearch --opensearch-url "http://localhost:9200" --index-name "my_index". Configuration can also be done via environment variables (OPENSEARCH_HOST, OPENSEARCH_HOSTPORT, INDEX_NAME). For local development, use fastmcp dev demo.py. Use with Claude Desktop by adding a configuration entry to claude_desktop_config.json.

Key features of mcp-server-opensearch

Semantic memory layer on OpenSearch
Single tool: search-openSearch
Supports query via JSON input
Configurable via CLI flags or environment variables
Integrates with Claude Desktop via MCP
Installation via Smithery or uv

Use cases of mcp-server-opensearch

Storing and retrieving LLM conversation memories in OpenSearch
Providing persistent context for AI assistants
Building a memory layer for custom AI workflows
Adding searchable memory to MCP‑compatible chat interfaces

常见问题

What is the Model Context Protocol?

MCP is an open protocol that enables seamless integration between LLM applications and external data sources and tools, providing a standardized way to connect LLMs with the context they need.

What dependencies are required?

The server uses `opensearch-py` with async support, but installation currently has a blocker: `pip install opensearch-py[async]` fails due to shell expansion. The recommended runtime is `uv`.

How do I configure the server?

You can pass `--opensearch-url`, `--opensearch-api-key`, and `--index-name` as command‑line arguments, or set the environment variables `OPENSEARCH_HOST`, `OPENSEARCH_HOSTPORT`, and `INDEX_NAME`.

What is the current development status?

The project is under construction; the async client for OpenSearch is not yet installing correctly, and work is ongoing to resolve that blocker.

How do I test the server?

Run the local OpenSearch client test with `uv run python src/mcp-server-opensearch/test_opensearch.py`, then test the MCP server connection with `cd src/mcp-server-opensearch && uv run fastmcp dev demo.py`.

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