cybersecurity-mcp-server
A security-first MCP (Model Context Protocol) backend built with Node.js and Express, featuring hardening, rate limiting, structured logging, and Docker support.
README
cybersecurity-mcp-server π
Security-first MCP (Model Context Protocol) backend built with Node.js and Express.
π Overview
This repository contains a secure, production-oriented MCP backend focusing on best practices for hardening, monitoring and safe defaults. It uses Express 5, Helmet for headers, rate limiting middleware, and structured logging (winston + winston-daily-rotate-file).
βοΈ Features
- Express 5 backend
- Security middleware (Helmet, rate limiting)
- Structured logging with
winstonandwinston-daily-rotate-file - Docker-friendly with a
Dockerfile - Simple, modular architecture (controllers, services, middleware)
π Project Structure
A short map of core folders and important files so contributors know where to look.
src/
ββ server.js # App entrypoint (starts HTTP server)
ββ app.js # Express app, middleware, routes registration
ββ routes/ # API route definitions (e.g., mcp.routes.js)
ββ controllers/ # Request handlers (mcp.controller.js)
ββ services/ # Business logic & integrations (mcp.service.js)
ββ middleware/ # auth, rateLimit, and other cross-cutting concerns
ββ config/ # env.js, security.js and config helpers
ββ utils/ # logger.js and helper utilities
logs/ # Rotated log files written by winston
Dockerfile # Container image build instructions
package.json # Scripts & dependencies
README.md # This document
Notes:
src/config/env.jscentralizes environment variable loading and defaults.utils/logger.jsuseswinston+winston-daily-rotate-filefor structured, rotated logs.- Keep business logic in
services/and handlers incontrollers/to keep tests and mocking straightforward.
π§βπ» Local Development
Below are platform-specific steps so contributors on Linux/macOS and Windows can get started quickly.
Linux / macOS
- Clone and install:
git clone https://github.com/Krishsakaria26/cybersecurity-mcp-server.git
cd cybersecurity-mcp-server
npm install
- Create a
.envfile (optional but recommended)
# copy example if present
cp .env.example .env || true
# or create manually
cat > .env <<EOF
PORT=3000
NODE_ENV=development
RATE_LIMIT_WINDOW_MS=60000
RATE_LIMIT_MAX=100
LOG_LEVEL=debug
EOF
- Start in development mode (auto-reloads on change)
npm run dev
- Production run
npm start
Tips:
- Use your IDE debugger attached to
src/server.jsfor breakpoints. - Add tests (Jest/Mocha) and a
testnpm script before merging features. - Use
pre-commithooks for linting/formatting consistency.
Windows (PowerShell / CMD / WSL)
For the closest parity with Linux/macOS behavior, use WSL or Git Bash. PowerShell and CMD work, but some shell behaviors differ (path separators, quoting).
- Clone and install (PowerShell / CMD):
git clone https://github.com/Krishsakaria26/cybersecurity-mcp-server.git
cd cybersecurity-mcp-server
npm install
- Create a
.envfile (PowerShell / CMD)
- PowerShell:
New-Item -Path . -Name '.env' -ItemType 'file' -Force
Add-Content -Path .env -Value 'PORT=3000'
Add-Content -Path .env -Value 'NODE_ENV=development'
Add-Content -Path .env -Value 'RATE_LIMIT_WINDOW_MS=60000'
Add-Content -Path .env -Value 'RATE_LIMIT_MAX=100'
Add-Content -Path .env -Value 'LOG_LEVEL=debug'
- CMD:
echo PORT=3000> .env
echo NODE_ENV=development>> .env
echo RATE_LIMIT_WINDOW_MS=60000>> .env
echo RATE_LIMIT_MAX=100>> .env
echo LOG_LEVEL=debug>> .env
- Start in development mode
npm run dev
Notes for Windows:
- If using Windows Defender / Firewall you'll be prompted when the server listens on a portβallow local access.
- If you need POSIX tools or to match Linux behavior (e.g., volume mounts in Docker), prefer using WSL or Git Bash.
π³ Docker Deployment
Docker commands differ slightly across shells; below are examples for both platforms and tips for Windows users.
Linux / macOS
Build the image:
docker build -t mcp-server:latest .
Run (basic):
docker run -p 3000:3000 --rm --name mcp-server mcp-server:latest
Run with env-file and persistent logs:
docker run -p 3000:3000 --env-file .env -v "$(pwd)/logs:/app/logs" --restart unless-stopped --name mcp-server mcp-server:latest
Windows (PowerShell / Docker Desktop)
Build the image (same):
docker build -t mcp-server:latest .
Run (basic):
docker run -p 3000:3000 --rm --name mcp-server mcp-server:latest
Run with env-file and persistent logs (PowerShell):
docker run -p 3000:3000 --env-file .\.env -v "${PWD}\logs:/app/logs" --restart unless-stopped --name mcp-server mcp-server:latest
If ${PWD} path doesn't mount correctly, provide the full Windows path for the volume mapping (replace C:\path\to\repo):
docker run -p 3000:3000 --env-file .\.env -v "C:\full\path\to\repo\logs:/app/logs" --restart unless-stopped --name mcp-server mcp-server:latest
Notes & recommendations:
- On Windows prefer Docker Desktop with WSL2 integration enabled for better compatibility.
- Use full absolute paths for volume mounts if relative mounts fail in PowerShell/CMD.
- When running containers in production, pass secrets via your orchestrator or Docker secrets (avoid committing
.env).
Optional: Docker Compose (works cross-platform if file paths are adjusted):
version: '3.8'
services:
mcp-server:
build: .
image: mcp-server:latest
ports:
- "3000:3000"
env_file: .env
volumes:
- ./logs:/app/logs
restart: unless-stopped
Health & deployment notes:
- Provide resource limits and environment secrets via your orchestrator (Kubernetes/Swarm).
- Mount
logs/for host access or forward logs to a log aggregator for production. - Consider adding a container healthcheck for orchestrators that rely on it.
π§© Configuration
Environment settings are loaded from src/config/env.js. Common variables:
PORT- server port (default: 3000)NODE_ENV- environment (development/production)- Rate limit options in
src/config/security.js(window, max requests)
Tip: Create a
.envfile in the project root for local development.
Logging
The app uses winston with winston-daily-rotate-file for rotated logs stored in the logs/ folder. Logs include structured timestamps and levels for easier parsing and aggregation.
π§ͺ Tests
There are no automated tests included yet. Contributions adding tests (Jest/Mocha) are welcomed.
π€ Contributing
Contributions are welcome β please open issues or pull requests on the GitHub repository. Follow existing code patterns and keep security considerations in mind.
π License
MIT β see the LICENSE file.
π€ Author
Krish Sakaria : Cybersecurity & Backend Engineering
Manish Shah : Backend Engineering
Madhusmita Choudhary : Fullstack Engineering
Aryan Karna : Frontend Developer
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.