How does the server handle security threat analysis?

It uses rule-based and AI-powered methods to detect SQL injection and monitor sensitive table access.

What interfaces are available to interact with the server?

The server supports STDIO, SSE, and REST API interfaces.

Is an API key required to run the server?

Yes, an OpenAI API key is required for the Text2SQL functionality.

Can the server provide real-time feedback on query processing?

Yes, it supports streaming responses for real-time feedback.

MCP AI SOC Sher

securitysqltext2sqlsocmonitoring

MCP server converting natural language to SQL with integrated AI-driven security threat analysis for SOC monitoring.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

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The install command below didn't complete successfully in our automated test.

uvx mcp-ai-soc-sher

× No solution found when resolving tool dependencies: ╰─▶ Because mcp-ai-soc-sher was not found in the package registry and you require mcp-ai-soc-sher, we can conclude that your requirements are unsatisfiable.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1mo ago.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "akramiot-mcp-ai-soc-sher": {
      "command": "uvx",
      "args": [
        "mcp-ai-soc-sher"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Overview

This MCP server provides a Text2SQL framework that dynamically converts natural language prompts into optimized SQL queries. It includes built-in security threat analysis to detect SQL injection and monitor sensitive table access, suitable for Security Operations Center (SOC) use cases. The server supports multiple interfaces including STDIO, SSE, and REST API, and can connect to SQLite or Snowflake databases.

Use cases

Convert natural language security queries to SQL

Analyze SQL queries for potential security threats

Monitor suspicious database activities in SOC environments

Stream real-time query processing feedback

Integrate with SQLite or Snowflake databases for security data

Key features

Text2SQL conversion from natural language

Rule-based and AI-powered SQL security analysis

Support for STDIO, SSE, and REST API interfaces

Multiple database support: SQLite and Snowflake

Real-time streaming responses

Configurable security monitoring and threat detection

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by MCP AI SOC Sher.

Extracted Tool Capabilities

Text2SQL conversion from natural language

Rule-based and AI-powered SQL security analysis

Support for STDIO, SSE, and REST API interfaces

Multiple database support: SQLite and Snowflake

Real-time streaming responses

Configurable security monitoring and threat detection

Documentation Overview

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

Terminal

pip install mcp-ai-soc-sher

Quick Start

server.ts

# 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

bash

# 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:

Code

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

server.ts

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

See LICENSE for details.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Read the full README →View source on GitHub →

Related MCP Servers

View all in Security View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

1y ago

Most recent push to the default branch.

Install check

Inconclusive

Install command did not finish in our automated test.

Directory activity

1 views

Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about MCP AI SOC Sher

It supports SQLite and Snowflake databases.

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Technical Specs & Signals

CategorySecurity

PricingBring your own API key (usage-based cost)

39Quality signal: Fair · 39/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools17/30

Adoption & activity2/15

Community engagement0/10

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No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 8d ago via OSV.dev · mcp-ai-soc-sher (PyPI)

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Overview

Use cases

Convert natural language security queries to SQL

Analyze SQL queries for potential security threats

Monitor suspicious database activities in SOC environments

Stream real-time query processing feedback

Integrate with SQLite or Snowflake databases for security data

Key features

Text2SQL conversion from natural language

Rule-based and AI-powered SQL security analysis

Support for STDIO, SSE, and REST API interfaces

Multiple database support: SQLite and Snowflake

Real-time streaming responses

Configurable security monitoring and threat detection

Documentation Overview

MCP AI SOC Sher

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

Terminal

pip install mcp-ai-soc-sher

Quick Start

server.ts

# 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

bash

# 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:

Code

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

server.ts

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

See LICENSE for details.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

1y ago

Most recent push to the default branch.

Install check

Inconclusive

Install command did not finish in our automated test.

Directory activity

1 views

Config copies, upvotes, and views on AllMCPs.