Apache Doris MCP Server

What is Apache Doris MCP Server about?

The server implements the Model Context Protocol (MCP) and exposes a set of tools that allow clients to execute SQL queries, retrieve metadata, perform data quality and lineage analysis, and leverage ADBC for high‑performance data transfer. It is designed for real‑time, OLAP workloads on Apache Doris and supports multi‑catalog environments.

How to use Apache Doris MCP Server?

1. Installation – install the package from PyPI:

   pip install doris-mcp-server
   

2. Configuration – set database connection details via environment variables (DORIS_HOST, DORIS_PORT, DORIS_USER, DORIS_PASSWORD) or command‑line arguments.
3. Start the server – choose a transport mode:
   • HTTP (Streamable):

     doris-mcp-server \
       --transport http \
       --host 0.0.0.0 \
       --port 3000 \
       --db-host 127.0.0.1 \
       --db-port 9030 \
       --db-user root \
       --db-password your_password
     

   • Stdio (for Cursor clients):

     doris-mcp-server --transport stdio
     

4. Client integration – use any MCP‑compatible client (e.g., Cursor, the bundled Python client) to call tools via the /mcp endpoint or stdio stream.

Key features of Apache Doris MCP Server

• MCP protocol implementation with unified HTTP and stdio communication.
• Tool manager centralizing 25+ tools: query execution, schema inspection, monitoring, data quality, lineage, freshness, slow‑query analysis, and ADBC support.
• Enterprise‑grade logging with level‑based files, automatic cleanup, and audit trails.
• Connection pool redesign eliminating at_eof errors and providing 99.9% stability.
• ADBC (Arrow Flight SQL) integration for 3‑10× faster large‑dataset transfers.
• Security framework: token/basic authentication, role‑based access control, SQL injection protection, data masking, and configurable blocked keywords.
• Catalog federation enabling multi‑catalog metadata access and three‑part table naming.
• Configurable global query timeout and fine‑grained performance settings.

Use cases of Apache Doris MCP Server

• Natural language analytics – LLMs translate user questions to SQL via exec_query.
• Data governance – automate data quality checks, column lineage tracing, and freshness monitoring.
• Performance monitoring – retrieve BE memory stats, slow‑query top‑N analysis, and capacity planning.
• High‑throughput data pipelines – use ADBC tools for fast bulk data extraction.
• Secure multi‑tenant analytics – enforce role‑based permissions and mask sensitive columns on‑the‑fly.

FAQ from the Apache Doris MCP Server

Q: How to configure database credentials? A: Use environment variables (DORIS_HOST, DORIS_PORT, DORIS_USER, DORIS_PASSWORD) or the corresponding CLI flags.

Q: What is the difference between HTTP and Stdio modes? A: HTTP runs as an independent web service supporting multiple concurrent clients; Stdio is a lightweight mode where the client directly manages the server process, ideal for Cursor.

Q: How are at_eof connection errors resolved? A: Version 0.5.x introduces a zero‑minimum‑connection pool, health monitoring, automatic retries, and self‑healing recovery, eliminating the issue.

Q: How to enable ADBC features? A: Set ADBC_ENABLED=true and configure FE_ARROW_FLIGHT_SQL_PORT / BE_ARROW_FLIGHT_SQL_PORT in .env. Then call exec_adbc_query or get_adbc_connection_info.

Q: How to customize logging retention? A: Adjust LOG_MAX_AGE_DAYS, LOG_CLEANUP_INTERVAL_HOURS, and ENABLE_LOG_CLEANUP in the environment file.

Q: How to restrict tool access per role? A: Define roles in the server’s security configuration (role_permissions dict) and map users via the authentication token.

Q: How to add a new tool? A: Implement the tool logic in doris_mcp_server/tools/tools_manager.py, register it with the @self.mcp.tool decorator, and define its input schema.

Doris MCP Server

Doris MCP (Model Context Protocol) Server is a backend service built with Python and FastAPI. It implements the MCP, 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.

🚀 What's New in v0.5.1

• 🔥 Critical at_eof Connection Fix: Complete elimination of at_eof connection pool errors through redesigned connection pool strategy with zero minimum connections, intelligent health monitoring, automatic retry mechanisms, and self-healing pool recovery - achieving 99.9% connection stability improvement
• 🔧 Revolutionary Logging System: Enterprise-grade logging overhaul with level-based file separation (debug, info, warning, error, critical), automatic cleanup scheduler with 30-day retention, millisecond precision timestamps, dedicated audit trails, and zero-maintenance log management
• 📊 Enterprise Data Analytics Suite: Introducing 7 new enterprise-grade data governance and analytics tools providing comprehensive data management capabilities including data quality analysis, column lineage tracking, freshness monitoring, and performance analytics
• 🏃‍♂️ High-Performance ADBC Integration: Complete Apache Arrow Flight SQL (ADBC) support with configurable parameters, offering 3-10x performance improvements for large dataset transfers through Arrow columnar format
• 🔄 Unified Data Quality Framework: Advanced data completeness and distribution analysis with business rules engine, confidence scoring, and automated quality recommendations
• 📈 Advanced Analytics Tools: Performance bottleneck identification, capacity planning with growth analysis, user access pattern monitoring, and data flow dependency mapping
• ⚙️ Enhanced Configuration Management: Complete ADBC configuration system with environment variable support, dynamic tool registration, and intelligent parameter validation
• 🔒 Security & Compatibility Improvements: Resolved pandas JSON serialization issues, enhanced enterprise security integration, and maintained full backward compatibility with v0.4.x versions
• 🎯 Modular Architecture: 6 new specialized tool modules for enterprise analytics with comprehensive English documentation and robust error handling
• 🕒 Global SQL Timeout Configuration Enhancement: Unified global SQL timeout control via config/performance/query_timeout. All SQL executions now use this value by default, with runtime override supported. This ensures consistent timeout behavior across all entry points (MCP tools, API, batch queries, etc.).
• Bug Fixes for Timeout Application: Fixed issues where some SQL executions did not correctly apply the global timeout configuration. Now, all SQL executions are consistently controlled by the global timeout setting.
• Improved Robustness: Optimized the timeout propagation chain in core classes like QueryRequest and DorisQueryExecutor, preventing timeout failures due to missing parameters.
• Documentation & Configuration Updates: Updated documentation and configuration instructions to clarify the priority and scope of the timeout configuration.
• Other Bug Fixes & Optimizations: Various known bug fixes and detail optimizations for improved stability and reliability.

🚀 Major Milestone: This release establishes v0.5.1 as a production-ready enterprise data governance platform with critical stability improvements (complete at_eof fix + intelligent logging + unified SQL timeout), 25 total tools (15 existing + 8 analytics + 2 ADBC tools), and enterprise-grade system reliability - representing a major advancement in both data intelligence capabilities and operational stability.

Core Features

• MCP Protocol Implementation: Provides standard MCP interfaces, supporting tool calls, resource management, and prompt interactions.
• Streamable HTTP Communication: Unified HTTP endpoint supporting both request/response and streaming communication for optimal performance and reliability.
• Stdio Communication: Standard input/output mode for direct integration with MCP clients like Cursor.
• Enterprise-Grade Architecture: Modular design with comprehensive functionality:
  • Tools Manager: Centralized tool registration and routing with unified interfaces (doris_mcp_server/tools/tools_manager.py)
  • Enhanced Monitoring Tools Module: Advanced memory tracking, metrics collection, and flexible BE node discovery with modular, extensible design
  • Query Information Tools: Enhanced SQL explain and profiling with configurable content truncation, file export for LLM attachments, and advanced query analytics
  • Resources Manager: Resource management and metadata exposure (doris_mcp_server/tools/resources_manager.py)
  • Prompts Manager: Intelligent prompt templates for data analysis (doris_mcp_server/tools/prompts_manager.py)
• Advanced Database Features:
  • Query Execution: High-performance SQL execution with advanced caching and optimization, enhanced connection stability and automatic retry mechanisms (doris_mcp_server/utils/query_executor.py)
  • Security Management: Comprehensive SQL security validation with configurable blocked keywords, SQL injection protection, data masking, and unified security configuration management (doris_mcp_server/utils/security.py)
  • Metadata Extraction: Comprehensive database metadata with catalog federation support (doris_mcp_server/utils/schema_extractor.py)
  • Performance Analysis: Advanced column analysis, performance monitoring, and data analysis tools (doris_mcp_server/utils/analysis_tools.py)
• Catalog Federation Support: Full support for multi-catalog environments (internal Doris tables and external data sources like Hive, MySQL, etc.)
• Enterprise Security: Comprehensive security framework with authentication, authorization, SQL injection protection, and data masking capabilities with environment variable configuration support
• Unified Configuration Framework: Centralized configuration management through config.py with comprehensive validation, standardized parameter naming, and smart default database handling with automatic fallback to information_schema

System Requirements

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

🚀 Quick Start

Installation from PyPI

# Install the latest version
pip install doris-mcp-server

# Install specific version
pip install doris-mcp-server==0.5.0

💡 Command Compatibility: After installation, both doris-mcp-server commands are available for backward compatibility. You can use either command interchangeably.

Start Streamable HTTP Mode (Web Service)

The primary communication mode offering optimal performance and reliability:

# Full configuration with database connection
doris-mcp-server \
    --transport http \
    --host 0.0.0.0 \
    --port 3000 \
    --db-host 127.0.0.1 \
    --db-port 9030 \
    --db-user root \
    --db-password your_password 

Start Stdio Mode (for Cursor and other MCP clients)

Standard input/output mode for direct integration with MCP clients:

# For direct integration with MCP clients like Cursor
doris-mcp-server --transport stdio

Verify Installation

# Check installation
doris-mcp-server --help

# Test HTTP mode (in another terminal)
curl http://localhost:3000/health

Environment Variables (Optional)

Instead of command-line arguments, you can use environment variables:

export DORIS_HOST="127.0.0.1"
export DORIS_PORT="9030"
export DORIS_USER="root"
export DORIS_PASSWORD="your_password"

# Then start with simplified command
doris-mcp-server --transport http --host 0.0.0.0 --port 3000

Command Line Arguments

The doris-mcp-server command supports the following arguments:

Argument	Description	Default	Required
--transport	Transport mode: http or stdio	http	No
--host	HTTP server host (HTTP mode only)	0.0.0.0	No
--port	HTTP server port (HTTP mode only)	3000	No
--db-host	Doris database host	localhost	No
--db-port	Doris database port	9030	No
--db-user	Doris database username	root	No
--db-password	Doris database password	-	Yes (unless in env)

Development Setup

For developers who want to build from source:

1. Clone the Repository

# Replace with the actual repository URL if different
git clone https://github.com/apache/doris-mcp-server.git
cd doris-mcp-server

2. Install Dependencies

pip install -r requirements.txt

3. Configure Environment Variables

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

cp .env.example .env

Key Environment Variables:

• Database Connection:
  • DORIS_HOST: Database hostname (default: localhost)
  • DORIS_PORT: Database port (default: 9030)
  • DORIS_USER: Database username (default: root)
  • DORIS_PASSWORD: Database password
  • DORIS_DATABASE: Default database name (default: information_schema)
  • DORIS_MIN_CONNECTIONS: Minimum connection pool size (default: 5)
  • DORIS_MAX_CONNECTIONS: Maximum connection pool size (default: 20)
  • DORIS_BE_HOSTS: BE nodes for monitoring (comma-separated, optional - auto-discovery via SHOW BACKENDS if empty)
  • DORIS_BE_WEBSERVER_PORT: BE webserver port for monitoring tools (default: 8040)
  • FE_ARROW_FLIGHT_SQL_PORT: Frontend Arrow Flight SQL port for ADBC (New in v0.5.0)
  • BE_ARROW_FLIGHT_SQL_PORT: Backend Arrow Flight SQL port for ADBC (New in v0.5.0)
• Security Configuration:
  • AUTH_TYPE: Authentication type (token/basic/oauth, default: token)
  • TOKEN_SECRET: Token secret key
  • ENABLE_SECURITY_CHECK: Enable/disable SQL security validation (default: true, New in v0.4.2)
  • BLOCKED_KEYWORDS: Comma-separated list of blocked SQL keywords (New in v0.4.2)
  • ENABLE_MASKING: Enable data masking (default: true)
  • MAX_RESULT_ROWS: Maximum result rows (default: 10000)
• ADBC Configuration (New in v0.5.0):
  • ADBC_DEFAULT_MAX_ROWS: Default maximum rows for ADBC queries (default: 100000)
  • ADBC_DEFAULT_TIMEOUT: Default ADBC query timeout in seconds (default: 60)
  • ADBC_DEFAULT_RETURN_FORMAT: Default return format - arrow/pandas/dict (default: arrow)
  • ADBC_CONNECTION_TIMEOUT: ADBC connection timeout in seconds (default: 30)
  • ADBC_ENABLED: Enable/disable ADBC tools (default: true)
• Performance Configuration:
  • ENABLE_QUERY_CACHE: Enable query caching (default: true)
  • CACHE_TTL: Cache time-to-live in seconds (default: 300)
  • MAX_CONCURRENT_QUERIES: Maximum concurrent queries (default: 50)
  • MAX_RESPONSE_CONTENT_SIZE: Maximum response content size for LLM compatibility (default: 4096, New in v0.4.0)
• Enhanced Logging Configuration (Improved in v0.5.0):
  • LOG_LEVEL: Log level (DEBUG/INFO/WARNING/ERROR, default: INFO)
  • LOG_FILE_PATH: Log file path (automatically organized by level)
  • ENABLE_AUDIT: Enable audit logging (default: true)
  • ENABLE_LOG_CLEANUP: Enable automatic log cleanup (default: true, Enhanced in v0.5.0)
  • LOG_MAX_AGE_DAYS: Maximum age of log files in days (default: 30, Enhanced in v0.5.0)
  • LOG_CLEANUP_INTERVAL_HOURS: Log cleanup check interval in hours (default: 24, Enhanced in v0.5.0)
  • New Features in v0.5.0:
    • Level-based File Separation: Automatic separation into debug.log, info.log, warning.log, error.log, critical.log
    • Timestamped Format: Enhanced formatting with millisecond precision and proper alignment
    • Background Cleanup Scheduler: Automatic cleanup with configurable retention policies
    • Audit Trail: Dedicated audit.log with separate retention management
    • Performance Optimized: Minimal overhead async logging with rotation support

Available MCP Tools

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

Tool Name	Description	Parameters
exec_query	Execute SQL query and return results.	sql (string, Required), db_name (string, Optional), catalog_name (string, Optional), max_rows (integer, Optional), timeout (integer, Optional)
get_table_schema	Get detailed table structure information.	table_name (string, Required), db_name (string, Optional), catalog_name (string, Optional)
get_db_table_list	Get list of all table names in specified database.	db_name (string, Optional), catalog_name (string, Optional)
get_db_list	Get list of all database names.	catalog_name (string, Optional)
get_table_comment	Get table comment information.	table_name (string, Required), db_name (string, Optional), catalog_name (string, Optional)
get_table_column_comments	Get comment information for all columns in table.	table_name (string, Required), db_name (string, Optional), catalog_name (string, Optional)
get_table_indexes	Get index information for specified table.	table_name (string, Required), db_name (string, Optional), catalog_name (string, Optional)
get_recent_audit_logs	Get audit log records for recent period.	days (integer, Optional), limit (integer, Optional)
get_catalog_list	Get list of all catalog names.	random_string (string, Required)
get_sql_explain	Get SQL execution plan with configurable content truncation and file export for LLM analysis.	sql (string, Required), verbose (boolean, Optional), db_name (string, Optional), catalog_name (string, Optional)
get_sql_profile	Get SQL execution profile with content management and file export for LLM optimization workflows.	sql (string, Required), db_name (string, Optional), catalog_name (string, Optional), timeout (integer, Optional)
get_table_data_size	Get table data size information via FE HTTP API.	db_name (string, Optional), table_name (string, Optional), single_replica (boolean, Optional)
get_monitoring_metrics_info	Get Doris monitoring metrics definitions and descriptions.	role (string, Optional), monitor_type (string, Optional), priority (string, Optional)
get_monitoring_metrics_data	Get actual Doris monitoring metrics data from nodes with flexible BE discovery.	role (string, Optional), monitor_type (string, Optional), priority (string, Optional)
get_realtime_memory_stats	Get real-time memory statistics via BE Memory Tracker with auto/manual BE discovery.	tracker_type (string, Optional), include_details (boolean, Optional)
get_historical_memory_stats	Get historical memory statistics via BE Bvar interface with flexible BE configuration.	tracker_names (array, Optional), time_range (string, Optional)
analyze_data_quality	Comprehensive data quality analysis combining completeness and distribution analysis.	table_name (string, Required), analysis_scope (string, Optional), sample_size (integer, Optional), business_rules (array, Optional)
trace_column_lineage	End-to-end column lineage tracking through SQL analysis and dependency mapping.	target_columns (array, Required), analysis_depth (integer, Optional), include_transformations (boolean, Optional)
monitor_data_freshness	Real-time data staleness monitoring with configurable freshness thresholds.	table_names (array, Optional), freshness_threshold_hours (integer, Optional), include_update_patterns (boolean, Optional)
analyze_data_access_patterns	User behavior analysis and security anomaly detection with access pattern monitoring.	days (integer, Optional), include_system_users (boolean, Optional), min_query_threshold (integer, Optional)
analyze_data_flow_dependencies	Data flow impact analysis and dependency mapping between tables and views.	target_table (string, Optional), analysis_depth (integer, Optional), include_views (boolean, Optional)
analyze_slow_queries_topn	Performance bottleneck identification with top-N slow query analysis and patterns.	days (integer, Optional), top_n (integer, Optional), min_execution_time_ms (integer, Optional), include_patterns (boolean, Optional)
analyze_resource_growth_curves	Capacity planning with resource growth analysis and trend forecasting.	days (integer, Optional), resource_types (array, Optional), include_predictions (boolean, Optional)
exec_adbc_query	High-performance SQL execution using ADBC (Arrow Flight SQL) protocol.	sql (string, Required), max_rows (integer, Optional), timeout (integer, Optional), return_format (string, Optional)
get_adbc_connection_info	ADBC connection diagnostics and status monitoring for Arrow Flight SQL.	No parameters required

Note: All metadata tools support catalog federation for multi-catalog environments. Enhanced monitoring tools provide comprehensive memory tracking and metrics collection capabilities. New in v0.5.0: 7 advanced analytics tools for enterprise data governance and 2 ADBC tools for high-performance data transfer with 3-10x performance improvements for large datasets.

4. Run the Service

Execute the following command to start the server:

./start_server.sh

This command starts the FastAPI application with Streamable HTTP MCP service.

5. Deploying on docker

If you want to run only Doris MCP Server in docker:

cd doris-mcp-server
docker build -t doris-mcp-server .
docker run -d -p <port>:<port> -v /*your-host*/doris-mcp-server/.env:/app/.env --name <your-mcp-server-name> -it doris-mcp-server:latest

Service Endpoints:

• Streamable HTTP: http://<host>:<port>/mcp (Primary MCP endpoint - supports GET, POST, DELETE, OPTIONS)
• Health Check: http://<host>:<port>/health

Note: The server uses Streamable HTTP for web-based communication, providing unified request/response and streaming capabilities.

Usage

Interaction with the Doris MCP Server requires an MCP Client. The client connects to the server's Streamable HTTP endpoint and sends requests according to the MCP specification to invoke the server's tools.

Main Interaction Flow:

1. Client Initialization: Send an initialize method call to /mcp (Streamable HTTP).
2. (Optional) Discover Tools: The client can call tools/list to get the list of supported tools, their descriptions, and parameter schemas.
3. Call Tool: The client sends a tools/call request, specifying the name and arguments.
   • Example: Get Table Schema
     • name: get_table_schema
     • arguments: Include table_name, db_name, catalog_name.
4. Handle Response:
   • Non-streaming: The client receives a response containing content or isError.
   • Streaming: The client receives a series of progress notifications, followed by a final response.

Catalog Federation Support

The Doris MCP Server supports catalog federation, enabling interaction with multiple data catalogs (internal Doris tables and external data sources like Hive, MySQL, etc.) within a unified interface.

Key Features:

• Multi-Catalog Metadata Access: All metadata tools (get_db_list, get_db_table_list, get_table_schema, etc.) support an optional catalog_name parameter to query specific catalogs.
• Cross-Catalog SQL Queries: Execute SQL queries that span multiple catalogs using three-part table naming.
• Catalog Discovery: Use get_catalog_list to discover available catalogs and their types.

Three-Part Naming Requirement:

All SQL queries MUST use three-part naming for table references:

• Internal Tables: internal.database_name.table_name
• External Tables: catalog_name.database_name.table_name

Examples:

1. Get Available Catalogs:

   {
     "tool_name": "get_catalog_list",
     "arguments": {"random_string": "unique_id"}
   }
   

2. Get Databases in Specific Catalog:

   {
     "tool_name": "get_db_list", 
     "arguments": {"random_string": "unique_id", "catalog_name": "mysql"}
   }
   

3. Query Internal Catalog:

   {
     "tool_name": "exec_query",
     "arguments": {
       "random_string": "unique_id",
       "sql": "SELECT COUNT(*) FROM internal.ssb.customer"
     }
   }
   

4. Query External Catalog:

   {
     "tool_name": "exec_query", 
     "arguments": {
       "random_string": "unique_id",
       "sql": "SELECT COUNT(*) FROM mysql.ssb.customer"
     }
   }
   

5. Cross-Catalog Query:

   {
     "tool_name": "exec_query",
     "arguments": {
       "random_string": "unique_id", 
       "sql": "SELECT i.c_name, m.external_data FROM internal.ssb.customer i JOIN mysql.test.user_info m ON i.c_custkey = m.customer_id"
     }
   }
   

Security Configuration

The Doris MCP Server includes a comprehensive security framework that provides enterprise-level protection through authentication, authorization, SQL security validation, and data masking capabilities.

Security Features

• 🔐 Authentication: Support for token-based and basic authentication
• 🛡️ Authorization: Role-based access control (RBAC) with security levels
• 🚫 SQL Security: SQL injection protection and blocked operations
• 🎭 Data Masking: Automatic sensitive data masking based on user permissions
• 📊 Security Levels: Four-tier security classification (Public, Internal, Confidential, Secret)

Authentication Configuration

Configure authentication in your environment variables:

# Authentication Type (token/basic/oauth)
AUTH_TYPE=token

# Token Secret for JWT validation
TOKEN_SECRET=your_secret_key_here

# Session timeout (in seconds)
SESSION_TIMEOUT=3600

Token Authentication Example

# Client authentication with token
auth_info = {
    "type": "token",
    "token": "your_jwt_token",
    "session_id": "unique_session_id"
}

Basic Authentication Example

# Client authentication with username/password
auth_info = {
    "type": "basic",
    "username": "analyst",
    "password": "secure_password",
    "session_id": "unique_session_id"
}

Authorization & Security Levels

The system supports four security levels with hierarchical access control:

Security Level	Access Scope	Typical Use Cases
Public	Unrestricted access	Public reports, general statistics
Internal	Company employees	Internal dashboards, business metrics
Confidential	Authorized personnel	Customer data, financial reports
Secret	Senior management	Strategic data, sensitive analytics

Role Configuration

Configure user roles and permissions:

# Example role configuration
role_permissions = {
    "data_analyst": {
        "security_level": "internal",
        "permissions": ["read_data", "execute_query"],
        "allowed_tables": ["sales", "products", "orders"]
    },
    "data_admin": {
        "security_level": "confidential", 
        "permissions": ["read_data", "execute_query", "admin"],
        "allowed_tables": ["*"]
    },
    "executive": {
        "security_level": "secret",
        "permissions": ["read_data", "execute_query", "admin"],
        "allowed_tables": ["*"]
    }
}

SQL Security Validation

The system automatically validates SQL queries for security risks:

Blocked Operations

Configure blocked SQL operations using environment variables (New in v0.4.2):

# Enable/disable SQL security check (New in v0.4.2)
ENABLE_SECURITY_CHECK=true

# Customize blocked keywords via environment variable (New in v0.4.2)
BLOCKED_KEYWORDS="DROP,DELETE,TRUNCATE,ALTER,CREATE,INSERT,UPDATE,GRANT,REVOKE,EXEC,EXECUTE,SHUTDOWN,KILL"

# Maximum query complexity score
MAX_QUERY_COMPLEXITY=100

Default Blocked Keywords (Unified in v0.4.2):

• DDL Operations: DROP, CREATE, ALTER, TRUNCATE
• DML Operations: DELETE, INSERT, UPDATE
• DCL Operations: GRANT, REVOKE
• System Operations: EXEC, EXECUTE, SHUTDOWN, KILL

SQL Injection Protection

The system automatically detects and blocks:

• Union-based injections: UNION SELECT attacks
• Boolean-based injections: OR 1=1 patterns
• Time-based injections: SLEEP(), WAITFOR functions
• Comment injections: --, /**/ patterns
• Stacked queries: Multiple statements separated by ;

Example Security Validation

# This query would be blocked
dangerous_sql = "SELECT * FROM users WHERE id = 1; DROP TABLE users;"

# This query would be allowed
safe_sql = "SELECT name, email FROM users WHERE department = 'sales'"

Data Masking Configuration

Configure automatic data masking for sensitive information:

Built-in Masking Rules

# Default masking rules
masking_rules = [
    {
        "column_pattern": r".*phone.*|.*mobile.*",
        "algorithm": "phone_mask",
        "parameters": {
            "mask_char": "*",
            "keep_prefix": 3,
            "keep_suffix": 4
        },
        "security_level": "internal"
    },
    {
        "column_pattern": r".*email.*", 
        "algorithm": "email_mask",
        "parameters": {"mask_char": "*"},
        "security_level": "internal"
    },
    {
        "column_pattern": r".*id_card.*|.*identity.*",
        "algorithm": "id_mask", 
        "parameters": {
            "mask_char": "*",
            "keep_prefix": 6,
            "keep_suffix": 4
        },
        "security_level": "confidential"
    }
]

Masking Algorithms

Algorithm	Description	Example
phone_mask	Masks phone numbers	138****5678
email_mask	Masks email addresses	j***n@example.com
id_mask	Masks ID card numbers	110101****1234
name_mask	Masks personal names	张*明
partial_mask	Partial masking with ratio	abc***xyz

Custom Masking Rules

Add custom masking rules in your configuration:

# Custom masking rule
custom_rule = {
    "column_pattern": r".*salary.*|.*income.*",
    "algorithm": "partial_mask",
    "parameters": {
        "mask_char": "*",
        "mask_ratio": 0.6
    },
    "security_level": "confidential"
}

Security Configuration Examples

Environment Variables

# .env file
AUTH_TYPE=token
TOKEN_SECRET=your_jwt_secret_key
ENABLE_MASKING=true
MAX_RESULT_ROWS=10000
BLOCKED_SQL_OPERATIONS=DROP,DELETE,TRUNCATE,ALTER
MAX_QUERY_COMPLEXITY=100
ENABLE_AUDIT=true

Sensitive Tables Configuration

# Configure sensitive tables with security levels
sensitive_tables = {
    "user_profiles": "confidential",
    "payment_records": "secret", 
    "employee_salaries": "secret",
    "customer_data": "confidential",
    "public_reports": "public"
}

Security Best Practices

1. 🔑 Strong Authentication: Use JWT tokens with proper expiration
2. 🎯 Principle of Least Privilege: Grant minimum required permissions
3. 🔍 Regular Auditing: Enable audit logging for security monitoring
4. 🛡️ Input Validation: All SQL queries are automatically validated
5. 🎭 Data Classification: Properly classify data with security levels
6. 🔄 Regular Updates: Keep security rules and configurations updated

Security Monitoring

The system provides comprehensive security monitoring:

# Security audit log example
{
    "timestamp": "2024-01-15T10:30:00Z",
    "user_id": "analyst_user",
    "action": "query_execution", 
    "resource": "customer_data",
    "result": "blocked",
    "reason": "insufficient_permissions",
    "risk_level": "medium"
}

⚠️ Important: Always test security configurations in a development environment before deploying to production. Regularly review and update security policies based on your organization's requirements.

Connecting with Cursor

You can connect Cursor to this MCP server using Stdio mode (recommended) or Streamable HTTP 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).

Method 1: Using PyPI Installation (Recommended)

Install the package from PyPI and configure Cursor to use it:

pip install doris-mcp-server

Configure Cursor: Add an entry like the following to your Cursor MCP configuration:

{
  "mcpServers": {
    "doris-stdio": {
      "command": "doris-mcp-server",
      "args": ["--transport", "stdio"],
      "env": {
        "DORIS_HOST": "127.0.0.1",
        "DORIS_PORT": "9030",
        "DORIS_USER": "root",
        "DORIS_PASSWORD": "your_db_password"
      }
    }
  }
}

Method 2: Using uv (Development)

If you have uv installed and want to run from source:

uv run --project /path/to/doris-mcp-server doris-mcp-server

Note: Replace /path/to/doris-mcp-server with the actual absolute path to your project directory.

Configure Cursor: Add an entry like the following to your Cursor MCP configuration:

{
  "mcpServers": {
    "doris-stdio": {
      "command": "uv",
      "args": ["run", "--project", "/path/to/your/doris-mcp-server", "doris-mcp-server"],
      "env": {
        "DORIS_HOST": "127.0.0.1",
        "DORIS_PORT": "9030",
        "DORIS_USER": "root",
        "DORIS_PASSWORD": "your_db_password"
      }
    }
  }
}

Streamable HTTP Mode

Streamable HTTP mode requires you to run the MCP server independently first, and then configure Cursor to connect to it.

1. Configure .env: Ensure your database credentials and any other necessary settings 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 reads the .env file and starts the FastAPI server with Streamable HTTP support. 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 Streamable HTTP endpoint:

   {
     "mcpServers": {
       "doris-http": {
          "url": "http://127.0.0.1:3000/mcp"
       }
     }
   }
   

   Note: Adjust the host/port if your server runs on a different address. The /mcp endpoint is the unified Streamable HTTP interface.

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

Directory Structure

doris-mcp-server/
├── doris_mcp_server/           # Main server package
│   ├── main.py                 # Main entry point and FastAPI app
│   ├── tools/                  # MCP tools implementation
│   │   ├── tools_manager.py    # Centralized tools management and registration
│   │   ├── resources_manager.py # Resource management and metadata exposure
│   │   ├── prompts_manager.py  # Intelligent prompt templates for data analysis
│   │   └── __init__.py
│   ├── utils/                  # Core utility modules
│   │   ├── config.py           # Configuration management with validation
│   │   ├── db.py               # Database connection management with pooling
│   │   ├── query_executor.py   # High-performance SQL execution with caching
│   │   ├── security.py         # Security management and data masking
│   │   ├── schema_extractor.py # Metadata extraction with catalog federation
│   │   ├── analysis_tools.py   # Data analysis and performance monitoring
│   │   ├── data_governance_tools.py  # Data lineage and freshness monitoring (New in v0.5.0)
│   │   ├── data_quality_tools.py     # Comprehensive data quality analysis (New in v0.5.0)
│   │   ├── data_exploration_tools.py # Advanced statistical analysis (New in v0.5.0)
│   │   ├── security_analytics_tools.py # Access pattern analysis (New in v0.5.0)
│   │   ├── dependency_analysis_tools.py # Impact analysis and dependency mapping (New in v0.5.0)
│   │   ├── performance_analytics_tools.py # Query optimization and capacity planning (New in v0.5.0)
│   │   ├── adbc_query_tools.py       # High-performance Arrow Flight SQL operations (New in v0.5.0)
│   │   ├── logger.py           # Logging configuration
│   │   └── __init__.py
│   └── __init__.py
├── doris_mcp_client/           # MCP client implementation
│   ├── client.py               # Unified MCP client for testing and integration
│   ├── README.md               # Client documentation
│   └── __init__.py
├── logs/                       # Log files directory
├── README.md                   # This documentation
├── .env.example                # Environment variables template
├── requirements.txt            # Python dependencies
├── pyproject.toml              # Project configuration and entry points
├── uv.lock                     # UV package manager lock file
├── generate_requirements.py    # Requirements generation script
├── start_server.sh             # Server startup script
└── restart_server.sh           # Server restart script

Developing New Tools

This section outlines the process for adding new MCP tools to the Doris MCP Server, based on the unified modular architecture with centralized tool management.

1. Leverage Existing Utility Modules

The server provides comprehensive utility modules for common database operations:

• doris_mcp_server/utils/db.py: Database connection management with connection pooling and health monitoring.
• doris_mcp_server/utils/query_executor.py: High-performance SQL execution with advanced caching, optimization, and performance monitoring.
• doris_mcp_server/utils/schema_extractor.py: Metadata extraction with full catalog federation support.
• doris_mcp_server/utils/security.py: Comprehensive security management, SQL validation, and data masking.
• doris_mcp_server/utils/analysis_tools.py: Advanced data analysis and statistical tools.
• doris_mcp_server/utils/config.py: Configuration management with validation.
• doris_mcp_server/utils/data_governance_tools.py: Data lineage tracking and freshness monitoring (New in v0.5.0).
• doris_mcp_server/utils/data_quality_tools.py: Comprehensive data quality analysis framework (New in v0.5.0).
• doris_mcp_server/utils/adbc_query_tools.py: High-performance Arrow Flight SQL operations (New in v0.5.0).

2. Implement Tool Logic

Add your new tool to the DorisToolsManager class in doris_mcp_server/tools/tools_manager.py. The tools manager provides a centralized approach to tool registration and execution with unified interfaces.

Example: Adding a new analysis tool:

# In doris_mcp_server/tools/tools_manager.py

async def your_new_analysis_tool(self, arguments: Dict[str, Any]) -> List[Dict[str, Any]]:
    """
    Your new analysis tool implementation
    
    Args:
        arguments: Tool arguments from MCP client
        
    Returns:
        List of MCP response messages
    """
    try:
        # Use existing utilities
        result = await self.query_executor.execute_sql_for_mcp(
            sql="SELECT COUNT(*) FROM your_table",
            max_rows=arguments.get("max_rows", 100)
        )
        
        return [{
            "type": "text",
            "text": json.dumps(result, ensure_ascii=False, indent=2)
        }]
        
    except Exception as e:
        logger.error(f"Tool execution failed: {str(e)}", exc_info=True)
        return [{
            "type": "text", 
            "text": f"Error: {str(e)}"
        }]

3. Register the Tool

Add your tool to the _register_tools method in the same class:

# In the _register_tools method of DorisToolsManager

@self.mcp.tool(
    name="your_new_analysis_tool",
    description="Description of your new analysis tool",
    inputSchema={
        "type": "object",
        "properties": {
            "parameter1": {
                "type": "string",
                "description": "Description of parameter1"
            },
            "parameter2": {
                "type": "integer", 
                "description": "Description of parameter2",
                "default": 100
            }
        },
        "required": ["parameter1"]
    }
)
async def your_new_analysis_tool_wrapper(arguments: Dict[str, Any]) -> List[Dict[str, Any]]:
    return await self.your_new_analysis_tool(arguments)

4. Advanced Features

For more complex tools, you can leverage the comprehensive framework:

• Advanced Caching: Use the query executor's built-in caching for enhanced performance
• Enterprise Security: Apply comprehensive SQL validation and data masking through the security manager
• Intelligent Prompts: Use the prompts manager for advanced query generation
• Resource Management: Expose metadata through the resources manager
• Performance Monitoring: Integrate with the analysis tools for monitoring capabilities

5. Testing

Test your new tool using the included MCP client:

# Using doris_mcp_client/client.py
from doris_mcp_client.client import DorisUnifiedMCPClient

async def test_new_tool():
    client = DorisUnifiedMCPClient()
    result = await client.call_tool("your_new_analysis_tool", {
        "parameter1": "test_value",
        "parameter2": 50
    })
    print(result)

MCP Client

The project includes a unified MCP client (doris_mcp_client/) for testing and integration purposes. The client supports multiple connection modes and provides a convenient interface for interacting with the MCP server.

For detailed client documentation, see doris_mcp_client/README.md.

Contributing

Contributions are welcome via Issues or Pull Requests.

License

This project is licensed under the Apache 2.0 License. See the LICENSE file for details.

FAQ

Q: Why do Qwen3-32b and other small parameter models always fail when calling tools?

A: This is a common issue. The main reason is that these models need more explicit guidance to correctly use MCP tools. It's recommended to add the following instruction prompt for the model:

• Chinese version：

<instruction>
尽可能使用MCP工具完成任务，仔细阅读每个工具的注解、方法名、参数说明等内容。请按照以下步骤操作：

1. 仔细分析用户的问题，从已有的Tools列表中匹配最合适的工具。
2. 确保工具名称、方法名和参数完全按照工具注释中的定义使用，不要自行创造工具名称或参数。
3. 传入参数时，严格遵循工具注释中规定的参数格式和要求。
4. 调用工具时，根据需要直接调用工具，但参数请求参考以下请求格式：{"mcp_sse_call_tool": {"tool_name": "$tools_name", "arguments": "{}"}}
5. 输出结果时，不要包含任何XML标签，仅返回纯文本内容。

<input>
用户问题：user_query
</input>

<output>
返回工具调用结果或最终答案，以及对结果的分析。
</output>
</instruction>

• English version：

<instruction>
Use MCP tools to complete tasks as much as possible. Carefully read the annotations, method names, and parameter descriptions of each tool. Please follow these steps:

1. Carefully analyze the user's question and match the most appropriate tool from the existing Tools list.
2. Ensure tool names, method names, and parameters are used exactly as defined in the tool annotations. Do not create tool names or parameters on your own.
3. When passing parameters, strictly follow the parameter format and requirements specified in the tool annotations.
4. When calling tools, call them directly as needed, but refer to the following request format for parameters: {"mcp_sse_call_tool": {"tool_name": "$tools_name", "arguments": "{}"}}
5. When outputting results, do not include any XML tags, return plain text content only.

<input>
User question: user_query
</input>

<output>
Return tool call results or final answer, along with analysis of the results.
</output>
</instruction>

If you have further requirements for the returned results, you can describe the specific requirements in the <output> tag.

Q: How to configure different database connections?

A: You can configure database connections in several ways:

1. Environment Variables (Recommended):

   export DORIS_HOST="your_doris_host"
   export DORIS_PORT="9030"
   export DORIS_USER="root"
   export DORIS_PASSWORD="your_password"
   

2. Command Line Arguments:

   doris-mcp-server --db-host your_host --db-port 9030 --db-user root --db-password your_password
   

3. Configuration File: Modify the corresponding configuration items in the .env file.

Q: How to configure BE nodes for monitoring tools?

A: Choose the appropriate configuration based on your deployment scenario:

External Network (Manual Configuration):

# Manually specify BE node addresses
DORIS_BE_HOSTS=10.1.1.100,10.1.1.101,10.1.1.102
DORIS_BE_WEBSERVER_PORT=8040

Internal Network (Automatic Discovery):

# Leave BE_HOSTS empty for auto-discovery
# DORIS_BE_HOSTS=  # Not set or empty
# System will use 'SHOW BACKENDS' command to get internal IPs

Q: How to use SQL Explain/Profile files with LLM for optimization?

A: The tools provide both truncated content and complete files for LLM analysis:

1. Get Analysis Results:

   {
     "content": "Truncated plan for immediate review",
     "file_path": "/tmp/explain_12345.txt",
     "is_content_truncated": true
   }
   

2. LLM Analysis Workflow:

   • Review truncated content for quick insights
   • Upload the complete file to your LLM as an attachment
   • Request optimization suggestions or performance analysis
   • Implement recommended improvements

3. Configure Content Size:

   MAX_RESPONSE_CONTENT_SIZE=4096  # Adjust as needed
   

Q: How to enable data security and masking features?

A: Set the following configurations in your .env file:

# Enable data masking
ENABLE_MASKING=true
# Set authentication type
AUTH_TYPE=token
# Configure token secret
TOKEN_SECRET=your_secret_key
# Set maximum result rows
MAX_RESULT_ROWS=10000

Q: What's the difference between Stdio mode and HTTP mode?

A:

• Stdio Mode: Suitable for direct integration with MCP clients (like Cursor), where the client manages the server process
• HTTP Mode: Independent web service that supports multiple client connections, suitable for production environments

Recommendations:

• Development and personal use: Stdio mode
• Production and multi-user environments: HTTP mode

Q: How to resolve connection timeout issues?

A: Try the following solutions:

1. Increase timeout settings:

   # Set in .env file
   QUERY_TIMEOUT=60
   CONNECTION_TIMEOUT=30
   

2. Check network connectivity:

   # Test database connection
   curl http://localhost:3000/health
   

3. Optimize connection pool configuration:

   DORIS_MAX_CONNECTIONS=20
   

Q: How to resolve at_eof connection errors? (Completely Fixed in v0.5.0)

A: Version 0.5.0 has completely resolved the critical at_eof connection errors through comprehensive connection pool redesign:

The Problem:

• at_eof errors occurred due to connection pool pre-creation and improper connection state management
• MySQL aiomysql reader state becoming inconsistent during connection lifecycle
• Connection pool instability under concurrent load

The Solution (v0.5.0):

1. Connection Pool Strategy Overhaul:

   • Zero Minimum Connections: Changed min_connections from default to 0 to prevent pre-creation issues
   • On-Demand Connection Creation: Connections created only when needed, eliminating stale connection problems
   • Fresh Connection Strategy: Always acquire fresh connections from pool, no session-level caching

2. Enhanced Health Monitoring:

   • Timeout-Based Health Checks: 3-second timeout for connection validation queries
   • Background Health Monitor: Continuous pool health monitoring every 30 seconds
   • Proactive Stale Detection: Automatic detection and cleanup of problematic connections

3. Intelligent Recovery System:

   • Automatic Pool Recovery: Self-healing pool with comprehensive error handling
   • Exponential Backoff Retry: Smart retry mechanism with up to 3 attempts
   • Connection-Specific Error Detection: Precise identification of connection-related errors

4. Performance Optimizations:

   • Pool Warmup: Intelligent connection pool warming for optimal performance
   • Background Cleanup: Periodic cleanup of stale connections without affecting active operations
   • Connection Diagnostics: Real-time connection health monitoring and reporting

Monitoring Connection Health:

# Monitor connection pool health in real-time
tail -f logs/doris_mcp_server_info.log | grep -E "(pool|connection|at_eof)"

# Check detailed connection diagnostics
tail -f logs/doris_mcp_server_debug.log | grep "connection health"

# View connection pool metrics
curl http://localhost:8000/health  # If running in HTTP mode

Configuration for Optimal Connection Performance:

# Recommended connection pool settings in .env
DORIS_MAX_CONNECTIONS=20          # Adjust based on workload
CONNECTION_TIMEOUT=30             # Connection establishment timeout
QUERY_TIMEOUT=60                  # Query execution timeout

# Health monitoring settings
HEALTH_CHECK_INTERVAL=60          # Pool health check frequency

Result: 99.9% elimination of at_eof errors with significantly improved connection stability and performance.

Q: How to resolve MCP library version compatibility issues? (Fixed in v0.4.2)

A: Version 0.4.2 introduced an intelligent MCP compatibility layer that supports both MCP 1.8.x and 1.9.x versions:

The Problem:

• MCP 1.9.3 introduced breaking changes to the RequestContext class (changed from 2 to 3 generic parameters)
• This caused TypeError: Too few arguments for RequestContext errors

The Solution (v0.4.2):

• Intelligent Version Detection: Automatically detects the installed MCP version
• Compatibility Layer: Gracefully handles API differences between versions
• Flexible Version Support: mcp>=1.8.0,<2.0.0 in dependencies

Supported MCP Versions:

# Both versions now work seamlessly
pip install mcp==1.8.0  # Stable version (recommended)
pip install mcp==1.9.3  # Latest version with new features

Version Information:

# Check which MCP version is being used
doris-mcp-server --transport stdio
# The server will log: "Using MCP version: x.x.x"

If you encounter MCP-related startup errors:

# Recommended: Use stable version
pip uninstall mcp
pip install mcp==1.8.0

# Or upgrade to latest compatible version
pip install --upgrade doris-mcp-server==0.5.0

Q: How to enable ADBC high-performance features? (New in v0.5.0)

A: ADBC (Arrow Flight SQL) provides 3-10x performance improvements for large datasets:

1. ADBC Dependencies (automatically included in v0.5.0+):

   # ADBC dependencies are now included by default in doris-mcp-server>=0.5.0
   # No separate installation required
   

2. Configure Arrow Flight SQL Ports:

   # Add to your .env file
   FE_ARROW_FLIGHT_SQL_PORT=8096
   BE_ARROW_FLIGHT_SQL_PORT=8097
   

3. Optional ADBC Customization:

   # Customize ADBC behavior (optional)
   ADBC_DEFAULT_MAX_ROWS=200000
   ADBC_DEFAULT_TIMEOUT=120
   ADBC_DEFAULT_RETURN_FORMAT=pandas  # arrow/pandas/dict
   

4. Test ADBC Connection:

   # Use get_adbc_connection_info tool to verify setup
   # Should show "status": "ready" and port connectivity
   

Q: How to use the new data analytics tools? (New in v0.5.0)

A: The 7 new analytics tools provide comprehensive data governance capabilities:

Data Quality Analysis:

{
  "tool_name": "analyze_data_quality",
  "arguments": {
    "table_name": "customer_data",
    "analysis_scope": "comprehensive",
    "sample_size": 100000
  }
}

Column Lineage Tracking:

{
  "tool_name": "trace_column_lineage", 
  "arguments": {
    "target_columns": ["users.email", "orders.customer_id"],
    "analysis_depth": 3
  }
}

Data Freshness Monitoring:

{
  "tool_name": "monitor_data_freshness",
  "arguments": {
    "freshness_threshold_hours": 24,
    "include_update_patterns": true
  }
}

Performance Analytics:

{
  "tool_name": "analyze_slow_queries_topn",
  "arguments": {
    "days": 7,
    "top_n": 20,
    "include_patterns": true
  }
}

Q: How to use the enhanced logging system? (Improved in v0.5.0)

A: Version 0.5.0 introduces a comprehensive logging system with automatic management and level-based organization:

Log File Structure (New in v0.5.0):

logs/
├── doris_mcp_server_debug.log      # DEBUG level messages
├── doris_mcp_server_info.log       # INFO level messages  
├── doris_mcp_server_warning.log    # WARNING level messages
├── doris_mcp_server_error.log      # ERROR level messages
├── doris_mcp_server_critical.log   # CRITICAL level messages
├── doris_mcp_server_all.log        # Combined log (all levels)
└── doris_mcp_server_audit.log      # Audit trail (separate)

Enhanced Logging Features:

1. Level-Based File Separation: Automatic organization by log level for easier troubleshooting
2. Timestamped Formatting: Millisecond precision with proper alignment for professional logging
3. Automatic Log Rotation: Prevents disk space issues with configurable file size limits
4. Background Cleanup: Intelligent cleanup scheduler with configurable retention policies
5. Audit Trail: Separate audit logging for compliance and security monitoring

Viewing Logs:

# View real-time logs by level
tail -f logs/doris_mcp_server_info.log     # General operational info
tail -f logs/doris_mcp_server_error.log    # Error tracking
tail -f logs/doris_mcp_server_debug.log    # Detailed debugging

# View all activity in combined log
tail -f logs/doris_mcp_server_all.log

# Monitor specific operations
tail -f logs/doris_mcp_server_info.log | grep -E "(query|connection|tool)"

# View audit trail
tail -f logs/doris_mcp_server_audit.log

Configuration:

# Enhanced logging configuration in .env
LOG_LEVEL=INFO                         # Base log level
ENABLE_AUDIT=true                      # Enable audit logging
ENABLE_LOG_CLEANUP=true                # Enable automatic cleanup
LOG_MAX_AGE_DAYS=30                    # Keep logs for 30 days
LOG_CLEANUP_INTERVAL_HOURS=24          # Check for cleanup daily

# Advanced settings
LOG_FILE_PATH=logs                     # Log directory (auto-organized)

Troubleshooting with Enhanced Logs:

# Debug connection issues
grep -E "(connection|pool|at_eof)" logs/doris_mcp_server_error.log

# Monitor tool performance
grep "execution_time" logs/doris_mcp_server_info.log

# Check system health
tail -20 logs/doris_mcp_server_warning.log

# View recent critical issues
cat logs/doris_mcp_server_critical.log

Log Cleanup Management:

• Automatic: Background scheduler removes files older than LOG_MAX_AGE_DAYS
• Manual: Logs are automatically rotated when they reach 10MB
• Backup: Keeps 5 backup files for each log level
• Performance: Minimal impact on server performance

For other issues, please check GitHub Issues or submit a new issue.