Alibaba Cloud AnalyticDB for PostgreSQL

What is AnalyticDB for PostgreSQL MCP Server?

AnalyticDB for PostgreSQL MCP Server is a universal interface that enables AI Agents to seamlessly communicate with and control Alibaba Cloud's AnalyticDB for PostgreSQL databases. It empowers AI Agents to retrieve database metadata, execute SQL operations, and utilize integrated GraphRAG and LLMemory capabilities.

How to use it?

You can integrate the server using two methods:

Local File Mode: Clone the repository and configure your MCP client to run the server from the local path. You'll need to provide your database connection details (host, port, user, password, database) and any necessary API keys for GraphRAG and LLMemory as environment variables.
PIP Mode: Install the server via pip (pip install adbpg_mcp_server) and configure your MCP client to run the installed command, passing the same database and API credentials as environment variables.

Key Features

AI Agent Integration: Connects AI agents directly to AnalyticDB for PostgreSQL.
Metadata Retrieval: Allows agents to fetch schema information about databases and tables.
SQL Execution: Enables agents to run SQL queries and perform data operations.
GraphRAG Integration: Leverages GraphRAG for advanced, graph-based data analysis and insights.
LLMemory Support: Integrates with LLMemory for enhanced memory and context management.
Secure Configuration: Uses environment variables to handle sensitive database and API credentials.

Use Cases

AI-Powered Data Analysis: Allow an AI agent to query and analyze data directly from your AnalyticDB for PostgreSQL.
Automated Database Tasks: Enable an AI agent to perform administrative or maintenance SQL tasks.
Advanced Data Insights: Utilize GraphRAG and LLMemory to uncover complex relationships and patterns in your data.
Data-Grounded Workflows: Integrate your database into larger AI-driven workflows that require access to your data.

FAQ

Q: What is the main purpose of this MCP server?

A: It acts as a bridge, allowing AI Agents to interact with Alibaba Cloud's AnalyticDB for PostgreSQL for tasks like data querying, metadata retrieval, and advanced analysis with GraphRAG and LLMemory.
Q: How do I configure the database connection?

A: Connection is configured by setting the ADBPG_HOST, ADBPG_PORT, ADBPG_USER, ADBPG_PASSWORD, and ADBPG_DATABASE environment variables in your MCP client setup. You may also need to configure API keys for GraphRAG and LLMemory.
Q: What are the prerequisites?

A: You need an active AnalyticDB for PostgreSQL instance on Alibaba Cloud and an MCP client to run the server.

AnalyticDB PostgreSQL MCP Server

AnalyticDB PostgreSQL MCP Server serves as a universal interface between AI Agents and AnalyticDB PostgreSQL databases. It enables seamless communication between AI Agents and AnalyticDB PostgreSQL, helping AI Agents retrieve database metadata and execute SQL operations.

Configuration

Mode 1: Download

Download from Github

git clone https://github.com/aliyun/alibabacloud-adbpg-mcp-server.git

MCP Integration

Add the following configuration to the MCP client configuration file:

"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",
      "GRAPHRAG_API_KEY": "graphrag llm api key",
      "GRAPHRAG_BASE_URL": "graphrag llm base url",
      "GRAPHRAG_LLM_MODEL": "graphrag llm model name",
      "GRAPHRAG_EMBEDDING_MODEL": "graphrag embedding model name",
      "GRAPHRAG_EMBEDDING_API_KEY": "graphrag embedding api key",
      "GRAPHRAG_EMBEDDING_BASE_URL": "graphrag embedding url",
      "LLMEMORY_API_KEY": "llm memory api_key",
      "LLMEMORY_BASE_URL": "llm memory base_url",
      "LLMEMORY_LLM_MODEL": "llm memory model name",
      "LLMEMORY_EMBEDDING_MODEL": "llm memory embedding model name"
    }
  }
}

Mode 2: Using pip

pip install adbpg_mcp_server

MCP Integration

"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",
      "GRAPHRAG_API_KEY": "graphrag api_key",
      "GRAPHRAG_BASE_URL": "graphrag base_url",
      "GRAPHRAG_LLM_MODEL": "graphrag model name",
      "GRAPHRAG_EMBEDDING_MODEL": "graphrag embedding model name",
      "GRAPHRAG_EMBEDDING_API_KEY": "graphrag embedding api key",
      "GRAPHRAG_EMBEDDING_BASE_URL": "graphrag embedding url",
      "LLMEMORY_API_KEY": "llm memory api_key",
      "LLMEMORY_BASE_URL": "llm memory base_url",
      "LLMEMORY_LLM_MODEL": "llm memory model name",
      "LLMEMORY_EMBEDDING_MODEL": "llm memory embedding model name"
    }
  }
}

Components

Tools

execute_select_sql: Execute SELECT SQL queries on the AnalyticDB PostgreSQL server
execute_dml_sql: Execute DML (INSERT, UPDATE, DELETE) SQL queries on the AnalyticDB PostgreSQL server
execute_ddl_sql: Execute DDL (CREATE, ALTER, DROP) SQL queries on the AnalyticDB PostgreSQL server
analyze_table: Collect table statistics
explain_query: Get query execution plan
adbpg_graphrag_upload
- Description: Upload a text file (with its name) and file content to graphrag to generate a knowledge graph.
- Parameters:
  - filename (text): The name of the file to be uploaded.
  - context (text): The textual content of the file.
adbpg_graphrag_query
- Description: Query the graphrag using the specified query string and mode.
- Parameters:
  - query_str (text): The query content.
  - query_mode (text): The query mode, choose from [bypass, naive, local, global, hybrid, mix]. If null, defaults to mix.
adbpg_graphrag_upload_decision_tree
- Description: Upload a decision tree with the specified root_node. If the root_node does not exist, a new decision tree will be created.
- Parameters:
  - context (text): The textual representation of the decision tree.
  - root_node (text): The content of the root node.
adbpg_graphrag_append_decision_tree
- Description: Append a subtree to an existing decision tree at the node specified by root_node_id.
- Parameters:
  - context (text): The textual representation of the subtree.
  - root_node_id (text): The ID of the node to which the subtree will be appended.
adbpg_graphrag_delete_decision_tree
- Description: Delete a sub-decision tree under the node specified by root_node_entity.
- Parameters:
  - root_node_entity (text): The ID of the root node of the sub-decision tree to be deleted.
adbpg_llm_memory_add
- Description: Add LLM long memory with a specific user, run or agent.
- Parameters:
  - messages (json): The messages.
  - user_id (text): User id.
  - run_id (text): Run id.
  - agent_id (text): Agent id.
  - metadata (json): The metadata json(optional).
  - memory_type (text): The memory type(optional).
  - prompt (text): The prompt(optional). Note:
    At least one of user_id, run_id, or agent_id should be provided.
adbpg_llm_memory_get_all
- Description: Retrieves all memory records associated with a specific user, run or agent.
- Parameters:
  - user_id (text): User ID.
  - run_id (text): Run ID.
  - agent_id (text): Agent ID. Note:
    At least one of user_id, run_id, or agent_id should be provided.
adbpg_llm_memory_search
- Description: Retrieves memories relevant to the given query for a specific user, run, or agent.
- Parameters:
  - query (text): The search query string.
  - user_id (text): User ID.
  - run_id (text): Run ID.
  - agent_id (text): Agent ID.
  - filter (json): Additional filter conditions in JSON format (optional). Note:
    At least one of user_id, run_id, or agent_id should be provided.
adbpg_llm_memory_delete_all:
- Description: Delete all memory records associated with a specific user, run or agent.
- Parameters:
  - user_id (text): User ID.
  - run_id (text): Run ID.
  - agent_id (text): Agent ID. Note:
    At least one of user_id, run_id, or agent_id should be provided.

Resources

Built-in Resources

adbpg:///schemas: Get all schemas in the database

Resource Templates

adbpg:///{schema}/tables: List all tables in a specific schema
adbpg:///{schema}/{table}/ddl: Get table DDL
adbpg:///{schema}/{table}/statistics: Show table statistics

Environment Variables

MCP Server requires the following environment variables to connect to AnalyticDB PostgreSQL instance:

ADBPG_HOST: Database host address
ADBPG_PORT: Database port
ADBPG_USER: Database username
ADBPG_PASSWORD: Database password
ADBPG_DATABASE: Database name

MCP Server requires the following environment variables to initialize graphRAG and llm memory server：

GRAPHRAG_API_KEY: API key for LLM provider
GRAPHRAG_BASE_URL: Base URL for LLM service endpoint
GRAPHRAG_LLM_MODEL: LLM model name or identifier
GRAPHRAG_EMBEDDING_MODEL: Embedding model name or identifier
GRAPHRAG_EMBEDDING_API_KEY: API key for embedding model provider
GRAPHRAG_EMBEDDING_BASE_URL: Base URL for embedding service endpoint
GRAPHRAG_LANGUAGE: (Optional)The language used by graphrag. Defaults to English if not set.
GRAPHRAG_ENTITY_TYPES: (Optional) Specifies the types of entity nodes to be extracted when parsing the document to generate the knowledge graph.
GRAPHRAG_RELATIONSHIP_TYPES: (Optional) Specifies the types of relationship edges to be extracted when parsing the document to generate the knowledge graph.
LLMEMORY_API_KEY: API key for LLM provider or embedding API
LLMEMORY_BASE_URL: Base URL for LLM or embedding service endpoint
LLMEMORY_LLM_MODEL: LLM model name or identifier
LLMEMORY_EMBEDDING_MODEL: Embedding model name or identifier

Dependencies

Python 3.10 or higher
Required packages:
- mcp >= 1.4.0
- psycopg >= 3.1.0
- python-dotenv >= 1.0.0
- pydantic >= 2.0.0

Running

# Create and activate virtual environment
uv venv .venv
source .venv/bin/activate  # Linux/Mac
# or
.venv\Scripts\activate     # Windows

# Install dependencies
uv pip install -e .

# Run server
uv run adbpg-mcp-server

Alibaba Cloud AnalyticDB for PostgreSQL