mcp-server-opensearch: An OpenSearch MCP Server
@MCP-Mirror
About mcp-server-opensearch: An OpenSearch MCP Server
Mirror of
Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"ibrooksSDX_mcp-server-opensearch": {
"command": "npx",
"args": [
"-y",
"@smithery/cli",
"install",
"@ibrooksSDX/mcp-server-opensearch",
"--client",
"claude"
]
}
}
}Tools
2`query` (json): prepared json query message
Confirmation message
Overview
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
FAQ from mcp-server-opensearch
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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