sec-mcp

A Python toolkit providing security checks for domains, URLs, IPs, and more. Integrate easily into any Python application, use via terminal CLI, or run as an MCP server to enrich LLM context with real-time threat insights.

2 stars PythonDeveloper Kits Updated Jun 17, 2025

mcp-serversecurity-tools

Documentation

sec-mcp: Security Checking Toolkit

Developed by Montimage, a company specializing in cybersecurity and network monitoring solutions.

Features
Installation
Quick Start
Usage
Performance Optimization
Benchmarking
Configuration
Development
License

Features

Comprehensive Security Checks: Validate domains, URLs, and IP addresses against multiple blacklist feeds
Multiple Threat Sources: OpenPhish, PhishTank, PhishStats, URLhaus, BlocklistDE, CINSSCORE, and more
High Performance: Ultra-fast in-memory storage with 1000-20,000x speedup over database-only approach
Smart Optimizations: Tiered lookup, URL normalization, and integer IPv4 storage for maximum efficiency
Flexible Integration: Use as Python library, CLI tool, or MCP server for LLM integration
Thread-Safe: SQLite storage with WAL mode and in-memory caching for concurrent operations
Auto-Updates: Scheduled daily updates from threat intelligence sources
Rich Monitoring: Built-in metrics, health checks, and performance tracking

Installation

bash

pip install sec-mcp

Requirements

Python 3.8+
SQLite 3
Optional: `pytricia` and `psutil` for benchmarking

Quick Start

1. Create a virtual environment (recommended):

bash

python3 -m venv .venv
   source .venv/bin/activate  # Windows: .venv\Scripts\activate.bat

2. Install sec-mcp:

bash

pip install sec-mcp

3. Initialize and update the database:

bash

sec-mcp update

4. Check the status:

bash

sec-mcp status

5. Check a URL:

bash

sec-mcp check https://example.com

Usage

CLI Usage

Single Check

bash

sec-mcp check https://example.com
sec-mcp check malicious-domain.com
sec-mcp check 192.168.1.1

Batch Check

bash

# From a file (one URL/domain/IP per line)
sec-mcp batch urls.txt

# With output to file
sec-mcp batch urls.txt --output results.json

Status and Updates

bash

# Check blacklist status
sec-mcp status

# Update blacklists
sec-mcp update

# Get detailed statistics
sec-mcp stats

Python API

python

from sec_mcp import SecMCP

# Initialize client
client = SecMCP()

# Update database (run once after installation)
client.update()

# Single check
result = client.check("https://example.com")
print(f"Safe: {result.is_safe}")
print(f"Source: {result.source}")

# Batch check
urls = ["https://example.com", "https://test.com", "192.168.1.1"]
results = client.check_batch(urls)
for r in results:
    print(f"{r.value}: {'SAFE' if r.is_safe else 'BLOCKED'}")

# Get statistics
status = client.get_status()
print(f"Total entries: {status.total_entries}")
print(f"Last update: {status.last_update}")

MCP Server

sec-mcp can run as an MCP server for AI/LLM integration (e.g., Claude, Windsurf, Cursor).

Setup

1. Install sec-mcp in a virtual environment (see Quick Start)

2. Update the blacklist:

bash

sec-mcp update

3. Configure your MCP client (e.g., `claude_desktop_config.json`):

json

{
     "mcpServers": {
       "sec-mcp": {
         "command": "/absolute/path/to/.venv/bin/python",
         "args": ["-m", "sec_mcp.start_server"],
         "env": {
           "MCP_USE_V2_STORAGE": "true"
         }
       }
     }
   }

> Important: Use the absolute path to your virtual environment's Python executable.

> - macOS/Linux: `/path/to/.venv/bin/python`

> - Windows: `C:\path\to\.venv\Scripts\python.exe`

Available MCP Tools

Tool Name	Description
`check_batch`	Check multiple domains/URLs/IPs in one call
`get_status`	Get blacklist status including entry counts and per-source breakdown
`update_blacklists`	Force immediate update of all blacklists
`get_diagnostics`	Get diagnostic info with modes: summary, full, health, performance, sample
`add_entry`	Manually add a blacklist entry
`remove_entry`	Remove a blacklist entry by URL or IP address

Note: The tools have been optimized to reduce token usage while maintaining full functionality. The `get_diagnostics` tool consolidates multiple monitoring functions with different modes.

Diagnostics Tool Modes

The `get_diagnostics` tool provides flexible monitoring with the following modes:

`summary` (default): Entry counts, sources, and last update times
`full`: Complete diagnostic data including health, stats, and performance
`health`: Database and scheduler health status only
`performance`: Performance metrics and hit rates (v2 storage only)
`sample`: Random sample of blacklist entries (use `sample_count` parameter)

Example usage:

python

# Get basic summary
await get_diagnostics()

# Check system health
await get_diagnostics(mode="health")

# Get performance metrics
await get_diagnostics(mode="performance")

# Sample 20 entries
await get_diagnostics(mode="sample", sample_count=20)

🚀 Performance Optimization

High-Performance Mode (v0.3.0+)

Enable ultra-fast in-memory storage for dramatic performance improvements:

bash

export MCP_USE_V2_STORAGE=true

Performance Comparison

Operation	v1 (Database)	v0.3.0 (Hybrid)	v0.4.0 (Optimized)	Speedup (vs v1)
Domain check	10ms	0.01ms	0.006ms	1,600x
URL check	5ms	0.001ms	0.0007ms	7,000x
IP + CIDR check	200ms	0.01ms	0.007ms	28,000x
Batch 100 items	2-3s	50-100ms	50-100ms	30x

Memory Usage

v1 (default): ~10MB (database on disk)
v0.3.0 (v2): ~60-80MB (in-memory for 125K entries)
v0.4.0 (v2 optimized): ~40-50MB (in-memory for 450K entries) - 30-40% reduction!

v0.4.0 Optimizations

1. Tiered Lookup (Hot/Cold Sources):

2. URL Normalization:

3. Integer IPv4 Storage:

Monitoring Performance

python

# Via MCP tool or Python API
metrics = client.get_storage_metrics()

# Returns:
{
  "total_lookups": 1234,
  "domain_lookups": 567,
  "url_lookups": 432,
  "ip_lookups": 235,
  "avg_lookup_time_ms": "0.0123",
  "memory_usage_mb": "45.3",
  "hit_rate": 0.89,
  "using_pytricia": true
}

Rollback to v1

bash

unset MCP_USE_V2_STORAGE
# or
export MCP_USE_V2_STORAGE=false

Benchmarking

Running Benchmarks

Compare performance across different storage implementations:

bash

# Install dependencies
pip install pytricia psutil

# Quick benchmark (10K entries, ~30 seconds)
./run_benchmark.sh --quick

# Standard benchmark (50K entries, ~2 minutes)
./run_benchmark.sh

# Full benchmark (100K entries, ~5 minutes)
./run_benchmark.sh --full --memory

# Compare specific versions
./run_benchmark.sh --v1 --v2opt    # Compare v1 vs v0.4.0
./run_benchmark.sh --all           # Compare all versions

Benchmark Options

Flag	Description
`--quick`	Quick benchmark with 10K entries (500 iterations)
`--full`	Full benchmark with 100K entries (1000 iterations)
`--all`	Compare all versions (v1, v0.3.0, v0.4.0)
`--v1`	Benchmark v1 (database-only storage)
`--v2`	Benchmark v0.3.0 (hybrid storage)
`--v2opt`	Benchmark v0.4.0 (optimized hybrid storage)
`--memory`	Include memory profiling (requires psutil)

Example Output

code

BENCHMARK RESULTS COMPARISON
================================================================================
Operation                 v1 (DB)         v0.3.0 (Hybrid)      v0.4.0 (Optimized)   Speedup
----------------------------------------------------------------------------------------------------
domain_lookup             9.8234ms        0.0098ms             0.0059ms             1,664x
url_lookup                4.5632ms        0.0009ms             0.0007ms             6,519x
ip_lookup                 198.2341ms      0.0103ms             0.0071ms             27,920x
batch_100                 2453.21ms       87.45ms              72.31ms              33.9x

v0.4.0 OPTIMIZATION METRICS
================================================================================
Domain Lookup             Hot source hit rate: 100.0%
URL Lookup                Hot source hit rate: 98.9%
IP Lookup                 Hot source hit rate: 88.9%

For detailed benchmarking instructions and methodology, see BENCHMARK_PLAYBOOK.md.

Configuration

Environment Variables

Variable	Description	Default
`MCP_DB_PATH`	Custom database location	Platform-specific (see below)
`MCP_USE_V2_STORAGE`	Enable high-performance mode	`false`

Default Database Locations

macOS: `~/Library/Application Support/sec-mcp/mcp.db`
Linux: `~/.local/share/sec-mcp/mcp.db`
Windows: `%APPDATA%\sec-mcp\mcp.db`

Custom Database Path

bash

export MCP_DB_PATH=/path/to/custom/location/mcp.db

Configuration File

Edit `config.json` to customize:

json

{
  "blacklist_sources": {
    "PhishTank": "https://...",
    "URLhaus": "https://..."
  },
  "update_time": "00:00",
  "cache_size": 10000,
  "log_level": "INFO"
}

Development

Setup Development Environment

bash

# Clone repository
git clone https://github.com/montimage/sec-mcp.git
cd sec-mcp

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install in development mode
pip install -e .

# Install development dependencies
pip install pytricia psutil pytest

Running Tests

bash

# Run all tests
pytest

# Run with coverage
pytest --cov=sec_mcp --cov-report=html

Project Structure

code

sec-mcp/
├── sec_mcp/              # Main package
│   ├── __init__.py
│   ├── storage.py        # v1 storage (database-only)
│   ├── storage_v2.py     # v2 storage (hybrid in-memory)
│   ├── start_server.py   # MCP server
│   └── cli.py           # CLI interface
├── benchmark.py          # Benchmark script
├── run_benchmark.sh      # Benchmark helper script
├── dev-docs/            # Development documentation (git-ignored)
├── tests/               # Test suite
└── README.md            # This file

License

MIT License - see LICENSE file for details.

About Montimage

sec-mcp is developed and maintained by Montimage, a company specializing in cybersecurity and network monitoring solutions. Montimage provides innovative security tools and services to help organizations protect their digital assets and ensure the security of their networks.

Support

Issues: GitHub Issues
Email: contact@montimage.eu
Website: https://www.montimage.eu

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository

2. Create your feature branch (`git checkout -b feature/amazing-feature`)

3. Commit your changes (`git commit -m 'Add some amazing feature'`)

4. Push to the branch (`git push origin feature/amazing-feature`)

5. Open a Pull Request

Acknowledgments

Threat intelligence sources: OpenPhish, PhishTank, PhishStats, URLhaus, BlocklistDE, CINSSCORE, and others
Built with Model Context Protocol (MCP)
Powered by Python and SQLite

Frequently asked questions

What is sec-mcp?

sec-mcp is A Python toolkit providing security checks for domains, URLs, IPs, and more. Integrate easily into any Python application, use via terminal CLI, or run as an MCP server to enrich LLM context with real-time threat insights.

How do I install sec-mcp?

Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.

Is sec-mcp open source?

Yes — it is hosted on GitHub at https://github.com/Montimage/sec-mcp and has 2 stars.

Related MCP tools

mcp

AWS MCP Servers — helping you get the most out of AWS, wherever you use MCP. Python-based implementation. Trusted by 6900+ developers.

6,922 Python

awsmcpmcp-client+6

mcpo

A simple, secure MCP-to-OpenAPI proxy server Python-based implementation. Trusted by 3500+ developers. Trusted by 3500+ developers.

3,594 Python

mcpmcp-servermcp-to-openapi+2

minimax-mcp

Official MiniMax Model Context Protocol (MCP) server that enables interaction with powerful Text to Speech, image generation and video generation APIs.

1,032 Python

image-generationimage-to-videomcp+7

mcpstore

开盒即用的优雅管理mcp服务 | 结合Agent框架 | 作者听劝 | 已发布pypi | Vue页面demo Python-based implementation.

375 Python

fastmcpmcpmcp-client+11

gpt-researcher

An LLM agent that conducts deep research (local and web) on any given topic and generates a long report with citations. Built for the Model Context Protocol to

24,026 Python

agentaiautomation+8

maxkb

🔥 MaxKB is an open-source platform for building enterprise-grade agents. MaxKB 是强大易用的开源企业级智能体平台。 for the Model Context Protocol. Enhance AI assistants with po

19,062 Python

agentagentic-aichatbot+11

Run your own MCP server? See who uses it and what to fix.

Measure it with TrackMCP

Documentation

sec-mcp: Security Checking Toolkit

Table of Contents

Features

Installation

Requirements

Quick Start

Usage

CLI Usage

Single Check

Batch Check

Status and Updates

Python API

MCP Server

Setup

Available MCP Tools

Diagnostics Tool Modes

🚀 Performance Optimization

High-Performance Mode (v0.3.0+)

Performance Comparison

Memory Usage

v0.4.0 Optimizations

Monitoring Performance

Rollback to v1

Benchmarking

Running Benchmarks

Benchmark Options

Example Output

Configuration

Environment Variables

Default Database Locations

Custom Database Path

Configuration File

Development

Setup Development Environment

Running Tests

Project Structure

License

About Montimage

Support

Contributing

Acknowledgments

Frequently asked questions

What is sec-mcp?

How do I install sec-mcp?

Is sec-mcp open source?

Related MCP tools