AnalyticDB PostgreSQL MCP Server by aliyun - MCP 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.

Installation

You can set up the server either from the source code for development or by installing it from PyPI for direct use.

Option 1: From Source (for Development)

This method is recommended if you want to modify or contribute to the server.

# 1. Clone the repository
git clone https://github.com/aliyun/alibabacloud-adbpg-mcp-server.git
cd alibabacloud-adbpg-mcp-server

# 2. Create and activate a virtual environment using uv
uv venv .venv
source .venv/bin/activate  # On Linux/macOS
# .\.venv\Scripts\activate  # On Windows

# 3. Install the project in editable mode
uv pip install -e .

Option 2: From PyPI (for Production/Usage)

This is the simplest way to install the server for direct use within your projects.

pip install adbpg-mcp-server

Running the Server

The server can be run in two transport modes: stdio (default) for integration with MCP clients, and http for direct API access or debugging.

Make sure you have set up the required Environment Variables before running the server.

Stdio Mode (Default)

This is the standard mode for communication with an MCP client.

# Run using the default transport (stdio)
uv run adbpg-mcp-server

# Or explicitly specify the transport
uv run adbpg-mcp-server --transport stdio

Streamable-HTTP Mode

This mode exposes an HTTP server, which is useful for testing, debugging, or direct integration via REST APIs.

# Run the server in HTTP mode on the default host and port (127.0.0.1:3000)
uv run adbpg-mcp-server --transport http

# Specify a custom host and port
uv run adbpg-mcp-server --transport http --host 0.0.0.0 --port 3000

MCP Integration

To integrate this server with a parent MCP client, add the following configuration to the client's configuration file. The arguments in the args array will depend on the transport protocol you choose.

Example for Stdio Transport

"mcpServers": {
  "adbpg-mcp-server": {
    "command": "uv",
    "args": [
      "run",
      "adbpg-mcp-server",
      "--transport",
      "stdio"
    ],
    "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",
      "LLMEMORY_ENABLE_GRAPH": "enable graph engine for llm memory (Default: false)"
    }
  }
}

Note: Since stdio is the default, you can optionally omit "--transport", "stdio" from the args array.

Example for Streamable-HTTP Transport

"mcpServers": {
  "adbpg-mcp-server": {
    "command": "uv",
    "args": [
      "run",
      "adbpg-mcp-server",
      "--transport",
      "http",
      "--port",
      "3000"
    ],
    "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",
      "LLMEMORY_ENABLE_GRAPH": "enable graph engine for llm memory (Default: false)"
    }
  }
}

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(context text, root_node text)
- 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(context text, root_node_id text)
- 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(root_node_entity text)
- 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.
- Parameters:
  - messages (json): The name of the file to be uploaded.
  - user_id (text): The user id.
  - run_id (text): The run id.
  - agent_id (text): The 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 (optional). If provided, fetch all memories for this user.
  - run_id (text): Run ID (optional).
  - agent_id (text): Agent ID (optional). If provided, fetch all memories for this agent. 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 (optional). If provided, fetch all memories for this user.
  - run_id (text): Run ID (optional).
  - agent_id (text): Agent ID (optional). If provided, fetch all memories for this agent.
  - 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 (optional). If provided, fetch all memories for this user.
  - run_id (text): Run ID (optional).
  - agent_id (text): Agent ID (optional). If provided, fetch all memories for this agent. Note:
    At least one of user_id, run_id, or agent_id should be provided.