aliyun_alibabacloud_tablestore_mcp_server

aliyun_alibabacloud_tablestore_mcp_server

by aliyun
A server implementation for integrating Tablestore with the Model Context Protocol (MCP) for large language models.

Tablestore MCP Server for Large Language Models

Overview

The Tablestore MCP Server is a powerful implementation of the Model Context Protocol (MCP), designed to seamlessly integrate Large Language Models (LLMs) with external data sources and tools. This server is particularly useful for developing AI-driven applications, enhancing chat interfaces, and creating customized AI workflows. It provides a standardized approach to connect LLMs with the critical contextual information they require.

Key Features

  • Seamless Integration: Connects LLMs with external data sources and tools efficiently.
  • Standardized Protocol: Implements the Model Context Protocol (MCP) for consistent and reliable integration.
  • Multi-language Support: Available in both Python and Java for flexible implementation.
  • Scalability: Designed to handle large-scale data and complex workflows.

Tools and Implementations

Python Implementation

The tablestore-python-mcp-server is a Python-based implementation of the MCP server. It is designed to be easy to use and integrate with existing Python applications.

View Python Implementation

Java Implementation

The tablestore-java-mcp-server is a Java-based implementation of the MCP server. It is ideal for Java developers looking to integrate LLMs with their applications.

View Java Implementation

Configuration

To configure the Tablestore MCP Server, follow the instructions provided in the respective README files for the Python and Java implementations. These guides will help you set up the server, connect it to your data sources, and start integrating it with your LLM applications.

Usage

Python

  1. Install Dependencies: Ensure all required Python packages are installed.
  2. Configure Server: Modify the configuration files to connect to your data sources.
  3. Run Server: Start the server using the provided scripts.

Java

  1. Build Project: Compile the Java project using Maven or Gradle.
  2. Configure Server: Adjust the configuration settings to connect to your data sources.
  3. Deploy Server: Deploy the server on your preferred Java application server.

Resources

License

This project is licensed under the Apache License 2.0. For more details, see the LICENSE file.

Activity

  • Stars: 9
  • Watchers: 3
  • Forks: 0

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Languages

  • Java: 56.6%
  • Python: 43.4%

Features & Capabilities

Categories
mcp_server model_context_protocol tablestore java python llm api_integration ai_workflows

Implementation Details

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Repository Info

aliyun Organization

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