Uses .env files for configuration management and storing API keys and database connection information
Utilizes OpenAI's API for Text2SQL conversion, enabling natural language queries to be transformed into SQL
Enables connection to Snowflake data warehouses for executing SQL queries generated from natural language
Connects to SQLite databases to execute generated SQL queries and retrieve data
MCP AI SOC Sher
A powerful AI-driven Security Operations Center (SOC) Text2SQL framework based MCP Server (Local and Remote) for converting natural language Prompts to SQL queries dynamically, with integrated security threat analysis and monitoring.
Features
Text2SQL Conversion: Convert natural language queries to optimized SQL
Multiple Interfaces: Support for STDIO, SSE, and REST API
Security Threat Analysis: Built-in SQL query security analysis
Multiple Database Support: Connect to SQLite or Snowflake databases
Streaming Responses: Real-time query processing feedback
SOC Monitoring: Security Operations Center monitoring capabilities
Installation
pip install mcp-ai-soc-sher
Quick Start
# Set your OpenAI API key
import os
os.environ["OPENAI_API_KEY"] = "your-api-key-here"
# Use as local server
from mcp_ai_soc_sher.local import LocalMCPServer
server = LocalMCPServer()
server.start()
# Or run from command line
# mcp-ai-soc --type local --stdio --sse
Command Line Usage
# Run local server with STDIO interface
mcp-ai-soc --type local --stdio
# Run local server with SSE interface
mcp-ai-soc --type local --sse
# Run remote server with REST API
mcp-ai-soc --type remote
Configuration
Create a .env file with your configuration:
OPENAI_API_KEY=your_openai_api_key_here
MCP_DB_URI=sqlite:///your_database.db
MCP_SECURITY_ENABLE_THREAT_ANALYSIS=true
See the documentation for all configuration options.
Example
import json
import requests
# Query the server
response = requests.post(
"http://localhost:8000/api/sql",
headers={"Content-Type": "application/json", "X-API-Key": "your-api-key"},
json={
"query": "Find all suspicious login attempts in the last 24 hours",
"optimize": True,
"execute": True
}
)
# Process the response
result = response.json()
print(f"SQL Query: {result['sql']}")
if result['results']:
print("Results:")
for row in result['results']:
print(row)
Security Features
Rule-based and AI-powered SQL query security analysis
Detection of potential SQL injection attacks
Sensitive table access monitoring
Configurable security levels and actions
License
MIT License with Additional Conditions. Copyright (c) 2025 Akram Sheriff.
See LICENSE for details.
Contributing
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
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