Cybersecurity MCPs

The Cybersecurity MCPs project is a curated collection of Model Context Protocol servers that empower AI assistants to perform specialized security tasks without leaving the conversational interface. By encapsulating each tool—SQL injection testing, network asset discovery, and Markdown‑to‑Word conversion—in its own lightweight server, developers can seamlessly integrate these capabilities into their AI workflows. The result is a modular ecosystem where security analysts and developers can invoke complex operations through natural language prompts, all while maintaining strict isolation between tools.

At its core, the project addresses a common pain point: the need for reliable, reproducible security tooling that can be called on demand by an AI. Traditional command‑line utilities or web interfaces require manual setup and context switching, which slows down investigations. The MCP servers expose a standard set of resources—such as scanning endpoints or document conversion jobs—that the AI can discover and interact with automatically. This removes friction, allowing analysts to focus on interpreting results rather than managing tools.

Key features include:

• Targeted security utilities: The server offers automated SQL injection scans and note management, while the provides a modernized interface to 360 Quake’s network asset search.
• Document processing: converts Markdown—including complex structures like nested lists, tables, and code blocks—into polished Word documents, supporting multilingual content such as Chinese.
• Developer-friendly architecture: Each implementation is self‑contained, written in TypeScript, and follows a consistent build/deploy workflow.
• Debugging support: Integration with MCP Inspector gives developers a web‑based interface to monitor requests, inspect payloads, and troubleshoot issues in real time.
• Easy integration: By adding a single configuration entry to Claude Desktop’s settings, users can launch any of the servers with minimal effort.

Real‑world scenarios that benefit from these MCPs include automated penetration testing pipelines, continuous security monitoring dashboards, and rapid generation of compliance documentation. For example, an AI assistant can prompt the to scan a newly discovered URL, then automatically document findings in a Word report via the . Meanwhile, the can surface hidden assets in a corporate network, feeding that data back into threat models or risk assessments.

What sets this collection apart is its focus on the cybersecurity domain combined with a modular, standards‑based approach. Developers who already work within the MCP ecosystem can drop these servers into their stack, immediately gaining powerful security tooling without rewriting any code. The result is a streamlined workflow where AI assistants act as the central command, orchestrating sophisticated security operations with precision and ease.