iflow-mcp_aliyun_adbpg-mcp-server 2.0.0
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.
Requires Python: >=3.11

# 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.

```shell
# 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.

```shell
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](#environment-variables) before running the server.

### Stdio Mode (Default)

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

```bash
# 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.

```bash
# 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 

```json
"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

```json
"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.

### 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：

- `API_KEY`: API key for LLM provider or embedding API
- `BASE_URL`: Base URL for LLM or embedding service endpoint
- `LLM_MODEL`: LLM model name or identifier
- `EMBEDDING_MODEL`: Embedding model name or identifier


## Dependencies

*   Python 3.11 or higher
*   `uv` (for environment and package management)