MCP Server Opensearch
This project is an implementation of an MCP server based on OpenSearch, providing semantic memory storage and retrieval functions for LLM applications such as Claude, and supporting the connection of AI tools and data sources through a standard protocol.
rating : 2.5 points
downloads : 24
What is the MCP server?
The MCP server is an open - protocol tool that allows large language models (LLMs) to integrate with external data sources. With this OpenSearch - implemented MCP server, you can easily store and retrieve semantic memories in a distributed search engine.How to use the MCP server?
You can set up and run the MCP server in just a few steps. First, install the necessary dependencies, then configure the connection to your OpenSearch instance. Finally, start storing and retrieving memories.Applicable scenarios
Suitable for applications that require efficient storage and retrieval of large amounts of text data, such as intelligent customer service, personalized recommendation systems, or knowledge graph construction.Main features
Store memoriesStore text data in the OpenSearch database for subsequent retrieval.
Retrieve memoriesRetrieve relevant memory records from the OpenSearch database based on query conditions.
Support for asynchronous operationsUtilize the OpenSearch asynchronous client to improve data processing efficiency.
Advantages and limitations
Advantages
Powerful distributed search capabilities
Easy to integrate with other systems
Efficient memory management mechanism
Limitations
Requires certain hardware resources to deploy the OpenSearch cluster
May be too complex for small - scale projects
How to use
Install dependencies
Ensure that Python and the uv tool are installed, then use pip to install the required dependency packages.
Start the server
Run the script to start the MCP server and specify the OpenSearch URL and index name.
Test the connection
Verify that the MCP server is successfully connected to the OpenSearch instance.
Usage examples
Example of storing memoriesStore a piece of text in OpenSearch for subsequent retrieval.
Example of retrieving memoriesRetrieve relevant information from the stored memories based on specific keywords.
Frequently Asked Questions
Why can't I install opensearch - py[async] in my environment?
How to verify that the MCP server is working properly?
Related resources
MCP official website
Understand the basic knowledge of the MCP protocol and its application scenarios.
OpenSearch official documentation
Get detailed technical documentation about OpenSearch.
GitHub repository
Access the project's source code and more examples.
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