Back to Catalog
Python
Security
AI SOC Security Threat analysis using MCP Server
MCP Trust Score
Based on our comprehensive evaluation criteria
🤖 Evaluated by gemini-2.5-flashFix
Trust Score59/100
- • Most MCP protocol features implemented (34/40)
- • Room for improvement in GitHub community
- • Good dependency choices (18/20)
- • Moderate deployment maturity (5/10)
- • Documentation (8/8)
- • Archestra MCP Trust score badge is missing
GitHub Metrics
Repository statistics and activity
⭐ GitHub Stars:2
👥 Contributors:1
📋 Total Issues:0
📦 Has Releases:Yes
🔧 Has CI/CD Pipeline:No
Configuration
Configuration example extracted from README.md for Claude Desktop and other clients.
🤖 Evaluated by gemini-2.5-flashFix
{
"mcp-ai-soc-sher-local-python": {
"command": "python",
"args": [
"-c",
"import os; os.environ[\"OPENAI_API_KEY\"] = \"your-api-key-here\"; from mcp_ai_soc_sher.local import LocalMCPServer; server = LocalMCPServer(); server.start()"
],
"env": {
"OPENAI_API_KEY": "your-api-key-here"
}
},
"mcp-ai-soc-local-stdio-sse": {
"command": "mcp-ai-soc",
"args": [
"--type",
"local",
"--stdio",
"--sse"
],
"env": {}
},
"mcp-ai-soc-local-stdio": {
"command": "mcp-ai-soc",
"args": [
"--type",
"local",
"--stdio"
],
"env": {}
},
"mcp-ai-soc-local-sse": {
"command": "mcp-ai-soc",
"args": [
"--type",
"local",
"--sse"
],
"env": {}
},
"mcp-ai-soc-remote": {
"command": "mcp-ai-soc",
"args": [
"--type",
"remote"
],
"env": {}
}
}MCP Protocol Support
Implemented MCP protocol features
🤖 Evaluated by gemini-2.5-flashFix
Tools:✓
Prompts:✓
Resources:✓
Sampling:✓
Roots:✗
Logging:✗
STDIO Transport:✓
HTTP Transport:✓
OAuth2 Auth:✗
Dependencies
15 dependencies
Libraries and frameworks used by this MCP server
🤖 Evaluated by gemini-2.5-flashFix
README.md
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.
Resources
Add Quality Badge
Show your MCP trust score in your README
[](https://archestra.ai/mcp-catalog/akramiot__mcp_ai_soc_sher)README.md
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.