10 MCP Servers for Cybersecurity Professionals and Elite Hackers

The Model Context Protocol (MCP) has emerged as a game-changing framework for extending AI capabilities, allowing Large Language Models (LLMs) to interface directly with specialized tools. For cybersecurity professionals and ethical hackers, this creates unprecedented opportunities to augment their workflows with AI-powered analysis, scanning, and reporting.

I've explored the most innovative MCP servers specifically designed for security tasks, from vulnerability scanning to network reconnaissance. These tools transform how security professionals can interact with complex security tooling through natural language interfaces, creating a powerful new paradigm for AI-assisted security work.

Source: Reddit.

Snyk's MCP integration: Official and community options

1. Official Snyk CLI MCP by Snyk

Snyk has officially integrated MCP support directly into their CLI, providing a standardized way for AI-enabled security tools to leverage Snyk's powerful scanning capabilities. This creates a connection between contextual AI understanding and concrete security scanning.

Tools Available

External APIs and Technologies

• Snyk CLI: The foundation of the integration; requires v1.1296.2 or later.

• SSE (Server-Sent Events): For HTTP-based transport.

• stdio (Standard IO): Alternative transport method.

Configuration Requirements

• SNYK_TOKEN: Your Snyk API token (can be set as an environment variable).

• SNYK_CFG_ORG: Optional configuration for specifying a specific Snyk organization.

Configuration Options

• Transport type: Choose between SSE (-t sse) or stdio (-t stdio) transport methods.

• Experimental flag: Must use the --experimental flag as the feature is in Early Access.

• Environment variables: All standard Snyk CLI environment variables are supported.

• Multiple integration methods: Supports configuration via IDE MCP hosts, mcpconfig.json, or direct CLI usage.

Snyk's official MCP implementation exemplifies how security tooling can be made more accessible through AI interfaces. By plugging directly into tools like Windsurf's MCP, Qodo's MCP support, or VS Code MCP extensions, security professionals can initiate vulnerability scanning through natural language queries, dramatically streamlining security workflows.

2. MCP-Snyk by punkpeye (1 ⭐ on Github)

This community implementation offers a lightweight alternative to running the full Snyk CLI, focusing specifically on repository and project scanning capabilities.

Tools available

External APIs and technologies

• Snyk API: For performing vulnerability scans and retrieving results.

• Node.js: Runtime environment for the server.

• TypeScript: For strongly-typed code structure.

• Zod: For schema validation.

• GitHub Actions: For automated build and test processes.

Configuration requirements

• SNYK_API_KEY: Required for authentication with Snyk's API.

Configuration Options

The server is configured primarily through Claude's desktop configuration:

{
  "mcpServers": {
    "snyk": {
      "command": "npx",
      "args": ["-y", "github:Sladey01/mcp-snyk"],
      "env": {
        "SNYK_API_KEY": "your_snyk_token_goes_here"
      }
    }
  }
}

This minimalist approach makes it perfect for quick security health checks of repositories you're considering integrating or evaluating.

3. MCP-Snyk by sammcj (10 ⭐ on Github)

A more comprehensive community-built Snyk integration offering expanded functionality beyond simple scanning.

Tools available

External APIs and technologies

• Snyk API: Core dependency for security scanning and project management

• Node.js: Server runtime environment

• TypeScript: Implementation language

• Zod: For input validation

• Snyk CLI (optional): Can use it to fetch the default organization ID

Configuration requirements

• SNYK_API_KEY: Essential for API authentication

• SNYK_ORG_ID (Optional): Your Snyk organization ID

Configuration options

This server offers a more flexible configuration with three methods for setting the organization ID:

1. MCP settings: Via environment variables in your MCP client configuration.

2. Snyk CLI configuration: Automatically detects if you've set the org via CLI.

3. Command arguments: Override defaults by specifying the org ID in commands.

This flexibility makes sammcj’s implementation particularly useful for enterprise users who may need to work across multiple Snyk organizations.

Network analysis and scanning tools

4. WireMCP by 0xKoda (95 ⭐ on Github)

WireMCP transforms your AI into a network analysis powerhouse by integrating the legendary Wireshark/tshark capabilities into the MCP ecosystem. It allows AI assistants to capture and analyze network traffic, check for threats, and extract insights from packet data.

Tools available

External APIs and technologies

• tshark (Wireshark): Core dependency for packet capture and analysis.

• Node.js: Runtime environment.

• axios: For making HTTP requests to threat intelligence feeds.

• which: Utility to locate the tshark executable.

• zod: Schema validation for inputs/outputs.

• URLhaus: External threat intelligence feed.

Configuration requirements

No specific API keys needed, but you must have tshark (Wireshark) installed and accessible in your system's PATH.

Configuration options

• tshark Path: Auto-detected but can be configured if needed.

• Runtime Parameters: Most tools accept parameters like interface (network interface to listen on) and duration (capture time)

WireMCP exemplifies how MCP can transform complex command-line tools into accessible capabilities for AI assistants. The ability to describe network behavior in natural language and have an AI interpret capture results creates powerful workflows for network debugging and security analysis.

5. nmap-mcp-server by phialsbasement (11 ⭐ on Github)

This server brings the legendary network mapper NMAP directly into your AI workflows, for natural language instructions and organic network reconnaissance.

Tools available

External APIs and technologies

• NMAP: The core network scanning engine.

• Node.js: Runtime environment (v18+).

• TypeScript: Implementation language.

• child_process: Node.js module for executing the NMAP command.

• Smithery: Compatible with Smithery for easier MCP server management.

Configuration requirements

• NMAP Installation: NMAP must be installed and accessible from the command line.

• Node.js: Version 18 or higher.

Configuration options

Configuration happens primarily on the client side, telling it how to launch the MCP server:

{
    "mcpServers": {
        "nmap": {
            "command": "node",
            "args": [
                "/path/to/mcp-nmap-server/dist/index.js"
            ]
        }
    }
}

The real power comes from the tool's runtime parameters:

• target: Host/network to scan (required).

• ports: Port specification (e.g., "80,443", "1-1000").

• scanType: "quick", "full", or "version".

• timing: NMAP timing template (0-5).

• additionalFlags: String for other NMAP options.

The elegant simplicity of this server—one tool with rich parameter options—makes it a powerful addition to security workflows, bringing NMAP's deep capabilities into conversational AI interfaces.

Development and code security tools

6. Semgrep MCP Server by Semgrep (132 ⭐ on Github)

This innovative server brings Semgrep's powerful static analysis capabilities directly into your AI workflow. It enables AI assistants to scan code for security vulnerabilities, create custom rules, and provide deep insights into code structure.

Tools available

External APIs and technologies

• Semgrep: The core static analysis engine.

• Python: Implementation language (3.10+).

• httpx: For asynchronous HTTP requests.

• FastAPI: For SSE transport implementation.

• uv: Fast Python package installer.

• Docker: For containerized deployment.

Configuration requirements

• SEMGREP_APP_TOKEN (Optional): For connecting to the Semgrep AppSec Platform.

• SEMGREP_URL (Optional): Can be overridden for self-hosted instances.

Configuration options

• Transport mode: Choose between stdio (default for CLI) or sse (default for Docker).

• Environment variables: Configure the Semgrep backend URL or authentication.

• Tool parameters: Configure code files, rulesets, and languages during tool calls.

Semgrep's MCP implementation demonstrates how sophisticated static analysis can be made accessible through natural language interfaces. The ability to have AI assistants perform deep code security checks and even help craft custom detection rules creates powerful new workflows for security engineers.

7. mcp-server-leetcode by doggybee (5 ⭐ on Github)

While primarily aimed at programmers looking to enhance their algorithm skills, this LeetCode MCP server has significant value for security professionals who recognize that strong coding fundamentals and algorithm knowledge are essential for security analysis and exploit development.

Tools available

External APIs and technologies

• LeetCode GraphQL API: Core data source.

• Node.js: Runtime environment.

• TypeScript: Implementation language.

• Axios: HTTP client for API requests.

• Zod: For parameter validation.

• Smithery: For packaging and distribution.

Configuration requirements

No API keys required—the server uses LeetCode's public GraphQL API.

Configuration options

• Claude desktop integration: Primarily configured through claude_desktop_config.json.

For security professionals, algorithmic thinking is a crucial skill for analyzing complex systems and developing exploits. This server creates an AI-powered pathway to building those foundational skills.

Infrastructure and container security

8. Container-MCP by 54rt1n (4 ⭐ on Github)

This innovative server creates a secure sandbox environment where LLMs can safely execute tools without risking the host system. It's particularly valuable for security professionals who need to analyze code, files, and web content in an isolated environment.

Tools available

External APIs and technologies

• Docker/Podman: For containerization.

• Firejail/AppArmor: For security sandboxing.

• Python 3.12: Core implementation.

• Playwright: For browser automation.

• BeautifulSoup4: For HTML parsing.

• Security Tools: The container includes numerous security tools (nikto, sqlmap, hydra, hashcat, john, etc.).

Configuration requirements

No explicit external API keys required.

Configuration options

Extensively configurable via environment variables:

• Server: Host, port, logging settings.

• Security: Enable/disable Firejail/AppArmor, command restrictions.

• Runtimes: Timeouts and memory limits for bash/Python.

• Web: Domain allowlists, timeouts.

The multi-layered security approach (container + Firejail + AppArmor) makes this an excellent platform for security professionals to safely analyze potentially malicious code or content while leveraging AI capabilities.

9. Awesome Kali MCP Servers by ccq1 (102 ⭐ on Github)

This comprehensive collection brings essential Kali Linux tools into the MCP ecosystem, creating AI-accessible interfaces for a wide range of security testing workflows.

Tools available

External APIs and technologies

• Docker: For containerization and sandboxing.

• FastMCP: Python framework for server implementation.

• Kali Linux Tools: Core security utilities (nmap, nm, objdump, strings, tshark, traceroute).

• Python: Implementation language.

Configuration requirements

No external API keys needed.

Configuration options

• Build Docker image: docker build -t kali-mcps:latest .

• Client configuration: Configure MCP clients to run the container.

• Sandbox mode: Optional environment variable IS_SAFE=true for sandboxed execution.

This comprehensive toolkit essentially brings the power of Kali Linux into AI-driven workflows, enabling security professionals to perform sophisticated analysis and testing through natural language instructions.

API testing and security

10. Postman MCP Server by delano (77 ⭐ on Github)

While primarily designed for API development and testing, this Postman integration is invaluable for security professionals who need to analyze, test, and secure APIs—a critical attack surface in modern applications.

Tools available

This server provides a comprehensive set of 80+ tools covering all aspects of Postman, including:

External APIs and technologies

• Postman API: Core API for accessing Postman resources.

• Axios: HTTP client for API requests.

• Zod: For schema validation.

• TypeScript: Implementation language.

Configuration requirements

• POSTMAN_API_KEY: Required for authenticating with the Postman API.

Configuration options

• POSTMAN_API_BASE_URL: Optional override for API endpoint (e.g., EU region).

• POSTMAN_RATE_LIMIT_WINDOW: Configure rate limiting behavior.

• POSTMAN_API_ACCEPT_HEADER: Customize the accept header for API version support.

For security professionals, this integration enables sophisticated API testing workflows, from discovering endpoints to examining request/response patterns and identifying potential vulnerabilities in API implementations.

Comparative analysis

Focus and capabilities

Tool availability

MCP ecosystem integration support

If a compatibility is crossed out, it may still work – it was just not an explicitly mentioned compatibility in the repo. 

Using these MCP Servers together

The real transformative power emerges when these MCP servers are combined to create sophisticated security workflows powered by AI assistance. Let's explore some potential synergies:

1. Full-stack application security assessment

Imagine conducting a comprehensive security assessment of a web application:

1. Use nmap-mcp-server to discover hosts, open ports, and running services.

2. Employ Semgrep MCP to scan application code for security vulnerabilities.

3. Leverage Snyk CLI MCP to check dependencies for known vulnerabilities.

4. Use Postman MCP to test the application's APIs for security issues.

5. Deploy WireMCP to analyze network traffic patterns and look for potential data leaks.

6. Use Container-MCP as a safe environment to test potentially malicious payloads.

This combination creates a complete security testing workflow, from discovery to exploitation testing, all orchestrated through natural language instructions to your AI assistant.

2. Continuous security monitoring

Set up an ongoing security monitoring system:

1. Regularly scan repositories with MCP-Snyk to catch new vulnerabilities.

2. Monitor network traffic with WireMCP to detect unusual patterns.

3. Check APIs with Postman MCP to ensure they're behaving as expected.

4. Use Awesome Kali MCP for periodic security tests.

By combining these tools, security professionals can maintain a continuous security posture with AI assistance, catching issues before they become critical.

3. Security research and exploit development

For advanced security research:

1. Use mcp-server-leetcode to strengthen algorithm skills. 😎

2. Analyze binaries with Awesome Kali MCP's binary analysis tools.

3. Employ Container-MCP as a safe environment for analyzing malware.

4. Test exploit concepts with Postman MCP against test APIs.

5. Validate findings with Semgrep MCP's code analysis.

This workflow enables security researchers to leverage AI assistance throughout the research process, from skill-building to exploit development and validation.

The future of AI-assisted security

The MCP servers we've explored represent the vanguard of a new paradigm in cybersecurity, one where AI assistants become active collaborators in security work rather than passive tools. By providing standardized interfaces to essential security tooling, these servers create an unprecedented opportunity for security professionals to amplify their capabilities.

As the MCP ecosystem continues to evolve, we can expect even more sophisticated integrations covering the full spectrum of security operations. Whether you're performing vulnerability assessment, penetration testing, incident response, or security research, these MCP servers offer a glimpse into a future where the boundary between human expertise and AI capabilities becomes increasingly fluid.

For cybersecurity professionals looking to stay at the cutting edge, exploring and integrating these MCP servers into existing workflows represents not just an efficiency gain, but a fundamental transformation in how security work gets done. Combining human creativity and judgment with AI-powered tool orchestration creates security capabilities greater than either could achieve alone.

As we navigate an increasingly complex threat landscape, this human-AI collaboration enabled by MCP represents our most promising path forward—a symbiotic relationship, where security professionals and AI assistants work hand-in-hand to defend our digital world. 🔐