Hologres MCP Server

Hologres MCP Server is a general interface service that connects AI agents with the Hologres database. It supports multiple communication modes and provides functions such as database metadata access, SQL execution, and statistical management.

Database Developer tools #Database interface #AI connector #SQL execution #Metadata management .Python

rating : 2.5 points

downloads : 7.4K

update time : 2025-04-28

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What is Hologres MCP Server?

Hologres MCP Server is a standardized interface that enables AI applications to easily access and operate data and functions in the Hologres database. It achieves seamless integration through multiple communication modes (such as STDIO, SSE, and HTTP Stream).

How to use Hologres MCP Server?

Simply set the necessary environment variables and start the server to begin interacting with the Hologres database. It supports functions such as executing SQL queries, obtaining table structures, and statistical information.

Applicable scenarios

It is suitable for application scenarios that require real - time data analysis, AI - driven database management, and cross - platform data collaboration.

Main features

Multi - mode transmission support

Supports three communication modes: STDIO, SSE, and HTTP Stream, adapting to different deployment environments.

Database metadata access

Obtain information about schemas, tables, and fields in the database.

SQL execution

Run SQL queries directly in Hologres and analyze the results.

Statistical management

Collect and view statistical information of tables.

Query plan generation

Obtain query plans and execution plans.

Advantages

Supports multiple communication protocols and has strong adaptability.

Simplifies the integration process between AI and the database.

Supports real - time data analysis and statistical management.

Provides detailed query plans and execution plans.

Limitations

Highly dependent on the environment, and network and permission configurations need to be ensured.

Complex queries may affect performance.

Some advanced functions may require additional configuration.

How to use

Install the server

Install Hologres MCP Server via Git cloning or PIP.

Configure environment variables

Set database connection information and the server port.

Start the server

Select the STDIO, SSE, or HTTP Stream mode according to your needs.

Usage examples

Example 1: Query table data

Execute an SQL query through the MCP Server and obtain the results.

Example 2: Get table structure

List all tables and their fields under the specified schema.

Frequently Asked Questions

How to switch communication modes?

How to verify that the server is running normally?

Related resources

Hologres MCP Server GitHub Repository

Project source code and documentation.

Model Context Protocol Official Website

Understand the standards and specifications of the MCP protocol.

Cherry Studio

An open - source AI client integrating the MCP Server.

🚀 Hologres MCP Server

Hologres MCP Server is an open - source project that adheres to the Model Context Protocol (MCP). It aims to enable AI applications to easily interact with the Hologres database.

🚀 Quick Start

📦 Installation and Running

# Clone the repository
git clone https://github.com/your-repository.git
cd hologres_mcp_server

# Set environment variables (optional)
export HOLOGRES_MCP_SERVER_ENV=development

# Start the server (SSE mode)
uv main:main --port 8001

# Or STDIO mode
uv main:main --transport stdio

# Or HTTP Stream mode
uv main:main --transport http-stream

🛠️ Environment Variables

Set the following environment variables to configure the server:

HOLOGRES_MCP_SERVER_ENV: Set the running environment, such as development or production.
PORT: Specify the port the server listens on, default is 8001.
DEBUG: Enable debug mode, with values true or false.

✨ Features

Hologres MCP Server supports the following three transport modes:

SSE (Server - Sent Events): Real - time communication via the WebSocket protocol.
HTTP Stream: Implement streaming responses using HTTP long - polling.
STDIO: Communicate directly through standard input and output.

🤝 Integration with Cherry Studio

Cherry Studio is an open - source AI client that supports MCP servers. To integrate it with Hologres MCP Server, follow these steps:

Download and install Cherry Studio: Visit the GitHub release page and download the version suitable for your system.
Configure the MCP service:
- SSE mode: Enter http://localhost:8001 in Cherry Studio.
- HTTP Stream mode: Enter http://localhost:8001/mcp in Cherry Studio.
- STDIO mode: Configure commands and parameters, referring to the configuration examples.

💻 Development

📋 Prerequisites

Python 3.10 or higher.
uv package management tool.

🏠 Local Development

Clone the repository.
Configure environment variables.
Run the server in development mode:

# SSE mode
python -m hologres_mcp_server.main

# STDIO mode
python -m hologres_mcp_server.main --transport stdio

# HTTP Stream mode
python -m hologres_mcp_server.main --transport http-stream

🧪 Testing

Use MCP Inspector for testing:

# Install MCP Inspector
npx @mcp/core@latest inspector

# Open the corresponding URL path in MCP Inspector:
# - SSE mode: http://localhost:8001
# - HTTP Stream mode: http://localhost:8001/mcp

📚 Documentation

Model Context Protocol (MCP)

MCP is a protocol for AI applications to interact with backend services. Hologres MCP Server supports the following features:

AI Integration: Support interaction with various AI models.
Data Processing: Capable of processing and analyzing data from the Hologres database.
Real - time Communication: Achieve low - latency real - time interaction through SSE or HTTP Stream.

📖 Glossary

SSE (Server - Sent Events): A technology that allows clients to receive real - time updates pushed by the server.
HTTP Stream: An HTTP - based long - polling technology for streaming data transfer.
STDIO: Standard input and output, referring to the way a program communicates through stdin and stdout.

By following the above steps and guidelines, you can easily integrate Hologres MCP Server into your project and leverage its powerful features to interact with AI applications.