MCP Vulnerability Scanner

The MCP Vulnerability Scanner fills a crucial gap for developers and security teams who want to integrate automated threat detection into AI‑driven workflows. By exposing a set of MCP tools that accept IP addresses as context items, the server lets an AI assistant query real‑time vulnerability data without leaving the chat or IDE. This means that a developer can ask the assistant to “scan my test server” and immediately receive a structured report detailing missing patches, known exploits, and recommended mitigations—all within the same conversational interface.

At its core, the server orchestrates two complementary scanning methods. The lightweight API‑based checks query public vulnerability databases for a given IP, while the optional Nmap integration performs active reconnaissance and port‑scanning to surface deeper weaknesses. The dual approach balances speed with depth: quick API lookups give instant feedback, and Nmap can be invoked when a more exhaustive audit is required. The results are returned as rich JSON objects that include severity levels, CVE identifiers, descriptive explanations, and actionable remediation steps. This structured format is ideal for downstream processing by other tools or for feeding into dashboards.

Key capabilities include:

• Single and batch scanning: handles one address, whereas accepts an array for parallel analysis, saving time in large network inventories.
• Context‑aware integration: The server declares as a supported context item type, allowing MCP clients to automatically supply relevant data from the user’s environment.
• Extensible reporting: Each report can be parsed, filtered, or visualized by the AI assistant, enabling custom workflows such as generating compliance checklists or auto‑creating GitHub issues for high‑severity findings.
• Docker and VS Code readiness: The server ships with Docker support and a VS Code integration configuration, making it trivial to deploy locally or as part of an IDE extension.

In practice, this MCP server is valuable for security‑focused development pipelines. For example, a continuous integration job can invoke on freshly built containers, and an AI assistant can summarize the findings for the team. Similarly, a DevOps engineer can ask the assistant to “scan all my staging IPs” and receive a consolidated risk report that feeds into a broader threat‑intelligence platform. The server’s design—leveraging MCP for seamless tool discovery and execution—ensures that these scans become first‑class citizens in AI‑augmented software delivery.