Hologres MCP Server Integration

What does “Hologres” MCP Server do?

Hologres MCP Server serves as a universal interface between AI Agents and Hologres databases. It facilitates seamless communication, enabling AI assistants and clients to retrieve metadata and execute SQL operations directly on Hologres databases. By acting as a bridge, the Hologres MCP Server empowers AI development workflows with the ability to perform complex database queries, manipulate data, and automate database-related tasks through standardized Model Context Protocol (MCP) endpoints. This integration streamlines access to enterprise-scale data stored in Hologres, supporting tasks such as metadata inspection, data analysis, real-time reporting, and more, all through secure and configurable MCP-based interactions.

List of Prompts

No prompt templates are explicitly mentioned in the repository or documentation.

List of Resources

No resources are explicitly described in the available documentation.

List of Tools

• execute_hg_select_sql: Executes a SQL SELECT query on the Hologres database, allowing retrieval of data based on custom queries.
• execute_hg_select_sql_with_serverless: Executes a SQL SELECT query on the Hologres database using serverless computing, enabling scalable and efficient data retrieval.
• execute_hg_dml_sql: Executes DML SQL queries (INSERT, UPDATE, DELETE) on the Hologres database, providing data manipulation capabilities.

Use Cases of this MCP Server

• Automated Data Analysis: AI agents can query Hologres databases for analytics, reporting, and data extraction, enabling rapid insights without manual SQL writing.
• Database Management: Developers can automate database maintenance tasks such as updating, inserting, or deleting records directly through LLM-powered workflows.
• Metadata Retrieval: AI assistants can inspect database schemas and metadata, assisting with data modeling and system integration tasks.
• Real-Time Data Integration: Integrate Hologres data into external services or dashboards, making enterprise data available for live applications.
• Serverless Query Processing: Leverage serverless execution for scalable and cost-effective querying, ideal for dynamic workloads or large-scale data operations.

How to set it up

Windsurf

1. Prerequisites: Ensure you have Python and the hologres-mcp-server package installed (pip install hologres-mcp-server).
2. Locate Configuration: Open the Windsurf client configuration file.
3. Add MCP Server: Insert the following JSON snippet into the mcpServers section:

   {
     "mcpServers": {
       "hologres-mcp-server": {
         "command": "uv",
         "args": [
           "run",
           "--with",
           "hologres-mcp-server",
           "hologres-mcp-server"
         ],
         "env": {
           "HOLOGRES_HOST": "host",
           "HOLOGRES_PORT": "port",
           "HOLOGRES_USER": "access_id",
           "HOLOGRES_PASSWORD": "access_key",
           "HOLOGRES_DATABASE": "database"
         }
       }
     }
   }
   

4. Save & Restart: Save your changes and restart Windsurf.
5. Verify: Test by issuing a query via the MCP client.

Claude

1. Prerequisites: Ensure hologres-mcp-server is installed and accessible.
2. Locate Configuration: Open Claude’s MCP client configuration.
3. Configure: Add the following:

   {
     "mcpServers": {
       "hologres-mcp-server": {
         "command": "uvx",
         "args": [
           "hologres-mcp-server"
         ],
         "env": {
           "HOLOGRES_HOST": "host",
           "HOLOGRES_PORT": "port",
           "HOLOGRES_USER": "access_id",
           "HOLOGRES_PASSWORD": "access_key",
           "HOLOGRES_DATABASE": "database"
         }
       }
     }
   }
   

4. Save & Restart: Save changes and restart Claude.
5. Test Integration: Execute a test command to ensure connectivity.

Cursor

1. Prerequisites: Install hologres-mcp-server via pip.
2. Edit Configuration: Open Cursor’s configuration file.
3. Insert MCP Server Config:

   {
     "mcpServers": {
       "hologres-mcp-server": {
         "command": "uv",
         "args": [
           "--directory",
           "/path/to/alibabacloud-hologres-mcp-server",
           "run",
           "hologres-mcp-server"
         ],
         "env": {
           "HOLOGRES_HOST": "host",
           "HOLOGRES_PORT": "port",
           "HOLOGRES_USER": "access_id",
           "HOLOGRES_PASSWORD": "access_key",
           "HOLOGRES_DATABASE": "database"
         }
       }
     }
   }
   

4. Save: Commit the changes and restart Cursor.
5. Verify: Confirm operation by running a SELECT query.

Cline

1. Install Prerequisites: Ensure hologres-mcp-server is installed.
2. Find Configuration: Open Cline’s MCP server config file.
3. Configure Server:

   {
     "mcpServers": {
       "hologres-mcp-server": {
         "command": "uv",
         "args": [
           "run",
           "--with",
           "hologres-mcp-server",
           "hologres-mcp-server"
         ],
         "env": {
           "HOLOGRES_HOST": "host",
           "HOLOGRES_PORT": "port",
           "HOLOGRES_USER": "access_id",
           "HOLOGRES_PASSWORD": "access_key",
           "HOLOGRES_DATABASE": "database"
         }
       }
     }
   }
   

4. Save & Restart: Save the file and restart Cline.
5. Test Setup: Confirm integration by executing a DML operation.

Securing API Keys

It is recommended to secure your database credentials and API keys using environment variables. You can set these in the env section:

"env": {
  "HOLOGRES_HOST": "your_host",
  "HOLOGRES_PORT": "your_port",
  "HOLOGRES_USER": "your_user",
  "HOLOGRES_PASSWORD": "your_password",
  "HOLOGRES_DATABASE": "your_database"
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "hologres-mcp-server": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “hologres-mcp-server” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Between the two tables, here’s a brief rating and reasoning:

The Hologres MCP Server provides solid database integration and clear documentation for setup and tooling. However, the absence of documented prompt templates, explicit resources, or sampling/roots support lowers its overall completeness as an MCP server. For database-focused use cases, it is robust, but it lacks some broader MCP features.

Our opinion

Rating: 6/10
This MCP server is reliable for its target use case (database interaction with Hologres) and provides clear setup instructions. Lack of prompt templates, resources, and advanced MCP features (sampling, roots) limits its flexibility for broader context-aware workflows.

MCP Score