MCP Server for OpenSearch is a basic Model Context Protocol (MCP) server designed to store and retrieve memories in the OpenSearch engine. It acts as a semantic memory layer on top of the OpenSearch database, enabling seamless integration between LLM applications and external data sources like OpenSearch.
This server provides a standardized way to connect Large Language Models (LLMs) with the context they need by leveraging OpenSearch's distributed search and analytics capabilities. It is particularly useful for building AI-powered IDEs, enhancing chat interfaces, or creating custom AI workflows that require integration with OpenSearch.
Developers and organizations working with LLM applications that require integration with OpenSearch for data storage, retrieval, and analytics would benefit from this tool. It is also useful for users of Claude Desktop who want to configure an MCP server for their workflows.
It can be used in environments where OpenSearch is deployed, such as local development setups, cloud-hosted instances, or enterprise-grade distributed systems. It integrates seamlessly with tools like Claude Desktop via configuration or the FastMCP UI.
This server should be implemented when there is a need to integrate LLM applications with OpenSearch for storing and retrieving context-based data. It is ideal for projects under development that require semantic memory layers and are already using or planning to use OpenSearch.
You can install it via Smithery using `npx -y @smithery/cli install @ibrooksSDX/mcp-server-opensearch --client claude` or run it directly using `uv` without installation: `uv run mcp-server-opensearch --opensearch-url 'http://localhost:9200' --index-name 'my_index'`.
The current blocker is related to installing the Async Client from OpenSearch. The command `pip install opensearch-py[async]` is not working as expected due to a 'no matches found' error in zsh.
You can test the connection locally by running `uv run python src/mcp-server-opensearch/test_opensearch.py` or by starting the MCP server with `uv run fastmcp dev demo.py`.
The required environment variables include `OPENSEARCH_HOST` (URL of the OpenSearch server), `OPENSEARCH_HOSTPORT` (port of the OpenSearch server), and `INDEX_NAME` (name of the index to use).
Yes, you can configure it in the `mcpServers` section of your `claude_desktop_config.json` file or install it via the FastMCP UI using `uv run fastmcp install demo.py`.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
An MCP Server is a server that supports the MCP protocol, enabling the exchange of contextual information between applications and AI models in a standardized way. It provides developers with an easy way to integrate AI models with databases, APIs, or other data sources.
An MCP Server eliminates the complexity of developing custom adapters by unifying the connection between AI models and various data sources. Whether you're a developer, data scientist, or AI app builder, an MCP Server simplifies the integration process, saving time and resources.
An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
At mcpserver.shop, you can browse our MCP Server Directory. The directory is categorized by industry (e.g., finance, healthcare, education), and each server comes with detailed descriptions and tags to help you quickly find the option that suits your needs.
The MCP Server Directory on mcpserver.shop is free to browse. However, some servers are hosted by third-party providers and may involve usage fees. Check the detailed page of each server for specific information.
MCP Servers support a wide range of data sources, including databases, APIs, cloud services, and custom tools. The flexibility of the MCP protocol allows it to connect almost any type of data source to AI models.
MCP Servers are primarily designed for developers, data scientists, and AI app builders. However, mcpserver.shop provides detailed documentation and guides to help users of varying technical levels get started easily.
Yes, MCP is an open-source protocol that encourages community participation and collaboration. For more details or to contribute, visit the official MCP documentation.
On mcpserver.shop, each MCP Server’s detailed page includes the provider’s contact information or a link. You can directly reach out to the provider for more details or technical support.