AnalyticDB PostgreSQL MCP Server

What does “AnalyticDB PostgreSQL” MCP Server do?

The AnalyticDB PostgreSQL MCP Server acts as a universal bridge between AI assistants and AnalyticDB PostgreSQL databases. It enables seamless interaction by allowing AI agents to retrieve database metadata, execute SQL queries, and manage database operations programmatically. By providing standardized access to database functionalities, this MCP server facilitates tasks such as schema exploration, query execution, collecting table statistics, and analyzing query performance. This makes it an essential tool for developers and data engineers looking to integrate AI-driven workflows with robust, enterprise-ready PostgreSQL analytics databases.

List of Prompts

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

List of Resources

• adbpg:///schemas
  Retrieves all schemas present in the connected AnalyticDB PostgreSQL database.

• adbpg:///{schema}/tables
  Lists all tables within a specified schema.

• adbpg:///{schema}/{table}/ddl
  Provides the Data Definition Language (DDL) statement for a specific table.

• adbpg:///{schema}/{table}/statistics
  Shows statistics related to a given table, aiding in performance analysis and optimization.

List of Tools

• execute_select_sql
  Executes SELECT SQL queries on the AnalyticDB PostgreSQL server to retrieve data.

• execute_dml_sql
  Executes DML (Data Manipulation Language) operations such as INSERT, UPDATE, or DELETE.

• execute_ddl_sql
  Executes DDL (Data Definition Language) operations like CREATE, ALTER, or DROP.

• analyze_table
  Collects statistics for a table to optimize database performance.

• explain_query
  Provides the execution plan for a given SQL query, helping users understand and optimize query performance.

Use Cases of this MCP Server

• Database Exploration and Metadata Retrieval
  Developers can easily explore database schemas, list tables, and access table definitions, improving productivity and understanding of data structures.

• Automated Query Execution
  AI agents can execute SELECT and DML queries programmatically, enabling use cases like report generation, data updates, and automated workflows.

• Schema Management and Evolution
  The server allows for executing DDL queries, facilitating schema changes such as creating, modifying, or dropping tables as part of CI/CD pipelines.

• Performance Tuning
  Tools like analyze_table and explain_query help developers gather statistics and execution plans, making it easier to identify bottlenecks and optimize queries.

• AI-driven Data Analysis
  Integrating with AI assistants, the server can support context-aware data analysis, enabling intelligent data exploration and insight generation.

How to set it up

Windsurf

1. Prerequisites:
   Ensure Python 3.10+ and required packages are installed.
2. Clone or Install:
   • Clone: git clone https://github.com/aliyun/alibabacloud-adbpg-mcp-server.git
   • Or install with pip: pip install adbpg_mcp_server
3. Edit Configuration:
   Open the Windsurf MCP client configuration file.
4. Add MCP Server:
   Insert the following JSON:

   "mcpServers": {
     "adbpg-mcp-server": {
       "command": "uv",
       "args": [
         "--directory",
         "/path/to/adbpg-mcp-server",
         "run",
         "adbpg-mcp-server"
       ],
       "env": {
         "ADBPG_HOST": "host",
         "ADBPG_PORT": "port",
         "ADBPG_USER": "username",
         "ADBPG_PASSWORD": "password",
         "ADBPG_DATABASE": "database"
       }
     }
   }
   

5. Save & Restart
   Save the file and restart Windsurf.

Claude

1. Prerequisites:
   Python 3.10+ and dependencies installed.
2. Install Server:
   pip install adbpg_mcp_server
3. Edit Configuration:
   Open Claude’s MCP configuration.
4. Add MCP Server:

   "mcpServers": {
     "adbpg-mcp-server": {
       "command": "uvx",
       "args": [
         "adbpg_mcp_server"
       ],
       "env": {
         "ADBPG_HOST": "host",
         "ADBPG_PORT": "port",
         "ADBPG_USER": "username",
         "ADBPG_PASSWORD": "password",
         "ADBPG_DATABASE": "database"
       }
     }
   }
   

5. Save & Restart
   Save configuration and restart Claude.

Cursor

1. Prerequisites:
   Ensure Python 3.10+ and dependencies.
2. Clone or Install:
   Clone or run pip install adbpg_mcp_server.
3. Edit Configuration:
   Open Cursor’s MCP configuration file.
4. Add MCP Server:

   "mcpServers": {
     "adbpg-mcp-server": {
       "command": "uvx",
       "args": [
         "adbpg_mcp_server"
       ],
       "env": {
         "ADBPG_HOST": "host",
         "ADBPG_PORT": "port",
         "ADBPG_USER": "username",
         "ADBPG_PASSWORD": "password",
         "ADBPG_DATABASE": "database"
       }
     }
   }
   

5. Save & Restart
   Save and restart Cursor.

Cline

1. Prerequisites:
   Python 3.10+ and dependencies.
2. Clone or Install:
   Use either Git or pip as above.
3. Edit Configuration:
   Open the MCP configuration.
4. Add MCP Server:

   "mcpServers": {
     "adbpg-mcp-server": {
       "command": "uvx",
       "args": [
         "adbpg_mcp_server"
       ],
       "env": {
         "ADBPG_HOST": "host",
         "ADBPG_PORT": "port",
         "ADBPG_USER": "username",
         "ADBPG_PASSWORD": "password",
         "ADBPG_DATABASE": "database"
       }
     }
   }
   

5. Save & Restart
   Save configuration and restart Cline.

Securing API Keys

Environment variables are used for database credentials. To enhance security, use environment variables instead of hardcoding sensitive information:

"env": {
  "ADBPG_HOST": "${ADBPG_HOST}",
  "ADBPG_PORT": "${ADBPG_PORT}",
  "ADBPG_USER": "${ADBPG_USER}",
  "ADBPG_PASSWORD": "${ADBPG_PASSWORD}",
  "ADBPG_DATABASE": "${ADBPG_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:

{
  "adbpg-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 “adbpg-mcp-server” to the actual name of your MCP server and replace the URL with your own MCP server URL.

Overview

Based on the available documentation, the AnalyticDB PostgreSQL MCP Server offers solid integration for database-driven workflows, with clear tools and resource endpoints. However, it lacks in areas such as prompt templates and explicit support for Roots/Sampling.

MCP Score

Opinion & Rating:
This MCP server is well-documented for its core database integration features, and covers essential developer needs for PostgreSQL. The absence of prompt templates and advanced MCP features like Roots or Sampling is a drawback, but its focus and clarity make it useful for database-oriented workflows. Rating: 7/10