Doris MCP Server

Doris MCP Server

Doris MCP (Model Control Panel) Server is a backend service built with Python and FastAPI. It implements the MCP (Model Control Panel) protocol, allowing clients to interact with it through defined "Tools". It's primarily designed to connect to Apache Doris databases, potentially leveraging Large Language Models (LLMs) for tasks like converting natural language queries to SQL (NL2SQL), executing queries, and performing metadata management and analysis.

Core Features

• MCP Protocol Implementation: Provides standard MCP interfaces, supporting tool calls, resource management, and prompt interactions.
• Multiple Communication Modes:
  • SSE (Server-Sent Events): Served via /sse (initialization) and /mcp/messages (communication) endpoints (src/sse_server.py).
  • Streamable HTTP: Served via the unified /mcp endpoint, supporting request/response and streaming (src/streamable_server.py).
  • (Optional) Stdio: Interaction possible via standard input/output (src/stdio_server.py), requires specific startup configuration.
• Tool-Based Interface: Core functionalities are encapsulated as MCP tools that clients can call as needed. Currently available key tools focus on direct database interaction:
  • SQL Execution (mcp_doris_exec_query)
  • Database and Table Listing (mcp_doris_get_db_list, mcp_doris_get_db_table_list)
  • Metadata Retrieval (mcp_doris_get_table_schema, mcp_doris_get_table_comment, mcp_doris_get_table_column_comments, mcp_doris_get_table_indexes)
  • Audit Log Retrieval (mcp_doris_get_recent_audit_logs) Note: Current tools primarily focus on direct DB operations.
• Database Interaction: Provides functionality to connect to Apache Doris (or other compatible databases) and execute queries (src/utils/db.py).
• Flexible Configuration: Configured via a .env file, supporting settings for database connections, LLM providers/models, API keys, logging levels, etc.
• Metadata Extraction: Capable of extracting database metadata information (src/utils/schema_extractor.py).

System Requirements

• Python 3.12+
• Database connection details (e.g., Doris Host, Port, User, Password, Database)

Quick Start

1. Clone the Repository

2. Install Dependencies

3. Configure Environment Variables

Copy the .env.example file to .env and modify the settings according to your environment:

Key Environment Variables:

• Database Connection:
  • DB_HOST: Database hostname
  • DB_PORT: Database port (default 9030)
  • DB_USER: Database username
  • DB_PASSWORD: Database password
  • DB_DATABASE: Default database name
• Server Configuration:
  • SERVER_HOST: Host address the server listens on (default 0.0.0.0)
  • SERVER_PORT: Port the server listens on (default 3000)
  • ALLOWED_ORIGINS: CORS allowed origins (comma-separated, * allows all)
  • MCP_ALLOW_CREDENTIALS: Whether to allow CORS credentials (default false)
• Logging Configuration:
  • LOG_DIR: Directory for log files (default ./logs)
  • LOG_LEVEL: Log level (e.g., INFO, DEBUG, WARNING, ERROR, default INFO)
  • CONSOLE_LOGGING: Whether to output logs to the console (default false)

Available MCP Tools

The following table lists the main tools currently available for invocation via an MCP client:

Note: All tools require a random_string parameter as a call identifier, typically handled automatically by the MCP client. "Optional" and "Required" refer to the tool's internal logic; the client might need to provide values for all parameters depending on its implementation. The tool names listed here are the base names; clients might see them prefixed (e.g., mcp_doris_stdio3_get_db_list) depending on the connection mode.

4. Run the Service

If you use SSE mode, execute the following command:

This command starts the FastAPI application, providing both SSE and Streamable HTTP MCP services by default.

Service Endpoints:

• SSE Initialization: http://<host>:<port>/sse
• SSE Communication: http://<host>:<port>/mcp/messages (POST)
• Streamable HTTP: http://<host>:<port>/mcp (Supports GET, POST, DELETE, OPTIONS)
• Health Check: http://<host>:<port>/health
• (Potential) Status Check: http://<host>:<port>/status (Confirm if implemented in main.py)

Usage

Interaction with the Doris MCP Server requires an MCP Client. The client connects to the server's SSE or Streamable HTTP endpoints and sends requests (like tool_call) according to the MCP specification to invoke the server's tools.

Main Interaction Flow:

1. Client Initialization: Connect to /sse (SSE) or send an initialize method call to /mcp (Streamable).
2. (Optional) Discover Tools: The client can call mcp/listTools or mcp/listOfferings to get the list of supported tools, their descriptions, and parameter schemas.
3. Call Tool: The client sends a tool_call message/request, specifying the tool_name and arguments.
   • Example: Get Table Schema
     • tool_name: mcp_doris_get_table_schema (or the mode-specific name)
     • arguments: Include random_string, table_name, db_name.
4. Handle Response:
   • Non-streaming: The client receives a response containing result or error.
   • Streaming: The client receives a series of tools/progress notifications, followed by a final response containing the result or error.

Specific tool names and parameters should be referenced from the src/tools/ code or obtained via MCP discovery mechanisms.

Connecting with Cursor

You can connect Cursor to this MCP server using either Stdio or SSE mode.

Stdio Mode

Stdio mode allows Cursor to manage the server process directly. Configuration is done within Cursor's MCP Server settings file (typically ~/.cursor/mcp.json or similar).

If you use stdio mode, please execute the following command to download and build the environment dependency package, but please note that you need to change the project path to the correct path address:

1. Configure Cursor: Add an entry like the following to your Cursor MCP configuration:
   { "mcpServers": { "doris-stdio": { "command": "uv", "args": ["--project", "/path/to/your/doris-mcp-server", "run", "doris-mcp"], "env": { "DB_HOST": "127.0.0.1", "DB_PORT": "9030", "DB_USER": "root", "DB_PASSWORD": "your_db_password", "DB_DATABASE": "your_default_db" } }, // ... other server configurations ... } }
2. Key Points:
   • Replace /path/to/your/doris-mcp with the actual absolute path to the project's root directory on your system. The --project argument is crucial for uv to find the pyproject.toml and run the correct command.
   • The command is set to uv (assuming you use uv for package management as indicated by uv.lock). The args include --project, the path, run, and mcp-doris (which should correspond to a script defined in your pyproject.toml).
   • Database connection details (DB_HOST, DB_PORT, DB_USER, DB_PASSWORD, DB_DATABASE) are set directly in the env block within the configuration file. Cursor will pass these to the server process. No .env file is needed for this mode when configured via Cursor.

SSE Mode

SSE mode requires you to run the MCP server independently first, and then tell Cursor how to connect to it.

1. Configure .env: Ensure your database credentials and any other necessary settings (like SERVER_PORT if not using the default 3000) are correctly configured in the .env file within the project directory.
2. Start the Server: Run the server from your terminal in the project's root directory:
   ./start_server.sh
   This script typically reads the .env file and starts the FastAPI server in SSE mode (check the script and sse_server.py / main.py for specifics). Note the host and port the server is listening on (default is 0.0.0.0:3000).
3. Configure Cursor: Add an entry like the following to your Cursor MCP configuration, pointing to the running server's SSE endpoint:
   { "mcpServers": { "doris-sse": { "url": "http://127.0.0.1:3000/sse" // Adjust host/port if your server runs elsewhere }, // ... other server configurations ... } }
   Note: The example uses the default port 3000. If your server is configured to run on a different port (like 3010 in the user example), adjust the URL accordingly.

After configuring either mode in Cursor, you should be able to select the server (e.g., doris-stdio or doris-sse) and use its tools.

Directory Structure

Developing New Tools

This section outlines the process for adding new MCP tools to the Doris MCP Server, considering the current project structure.

1. Leverage Utility Modules

Before writing new database interaction logic from scratch, check the existing utility modules:

• doris_mcp_server/utils/db.py: Provides basic functions for getting database connections (get_db_connection) and executing raw queries (execute_query, execute_query_df).
• doris_mcp_server/utils/schema_extractor.py (MetadataExtractor class): Offers high-level methods to retrieve database metadata, such as listing databases/tables (get_all_databases, get_database_tables), getting table schemas/comments/indexes (get_table_schema, get_table_comment, get_column_comments, get_table_indexes), and accessing audit logs (get_recent_audit_logs). It includes caching mechanisms.
• doris_mcp_server/utils/sql_executor_tools.py (execute_sql_query function): Provides a wrapper around db.execute_query that includes security checks (optional, controlled by ENABLE_SQL_SECURITY_CHECK env var), adds automatic LIMIT to SELECT queries, handles result serialization (dates, decimals), and formats the output into the standard MCP success/error structure. It's recommended to use this for executing user-provided or generated SQL.

You can import and combine functionalities from these modules to build your new tool.

2. Implement Tool Logic

Implement the core logic for your new tool as an async function within doris_mcp_server/tools/mcp_doris_tools.py. This keeps the primary tool implementations centralized. Ensure your function returns data in a format that can be easily wrapped into the standard MCP response structure (see _format_response in the same file for reference).

Example: Let's create a simple tool get_server_time.

3. Register the Tool (Dual Registration)

Due to the separate handling of SSE/Streamable and Stdio modes, you need to register the tool in two places:

A. SSE/Streamable Registration (tool_initializer.py)

• Import your new tool function from mcp_doris_tools.py.
• Inside the register_mcp_tools function, add a new wrapper function decorated with @mcp.tool().
• The wrapper function should call your core tool function.
• Define the tool name and provide a detailed description (including parameters if any) in the decorator. Remember to include the mandatory random_string parameter description for client compatibility, even if your wrapper doesn't explicitly use it.

Example (tool_initializer.py):

B. Stdio Registration (mcp_core.py)

• Similar to SSE, add a new wrapper function decorated with @stdio_mcp.tool().
• Important: Import your core tool function (mcp_doris_get_server_time) inside the wrapper function (delayed import pattern used in this file).
• The wrapper calls the core tool function. The wrapper itself might need to be async def depending on how FastMCP handles tools in Stdio mode, even if the underlying function is simple (as seen in the current file structure). Ensure the call matches (e.g., use await if calling an async function).

Example (mcp_core.py):

4. Restart and Test

After implementing and registering the tool in both files, restart the MCP server (both SSE mode via ./start_server.sh and ensure the Stdio command used by Cursor is updated if necessary) and test the new tool using your MCP client (like Cursor) in both connection modes.

Contributing

Contributions are welcome via Issues or Pull Requests.

License

This project is licensed under the Apache 2.0 License. See the LICENSE file (if it exists) for details.