VulniCheck MCP Server

VulniCheck – AI‑Powered Security Scanning for Python Projects

VulniCheck tackles the growing challenge of keeping modern Python codebases free from known and emerging security weaknesses. Traditional static analysis tools can quickly become outdated or miss context‑specific risks, while manual reviews are time‑consuming. By combining a robust vulnerability database layer with AI‑driven risk assessment, VulniCheck delivers actionable insights that developers can apply immediately. The result is a single, Docker‑based MCP server that turns routine code inspections into comprehensive security audits without the need for custom scripting or infrastructure.

The server exposes a rich set of tools that can be invoked directly from an AI assistant such as Claude. A developer simply asks the assistant to “scan my project” or “check dependencies for vulnerabilities,” and VulniCheck orchestrates the entire workflow: it parses , , or installed packages; queries multiple vulnerability feeds (OSV.dev, NVD, GitHub Advisory, CIRCL, Safety DB); and aggregates findings. The AI layer then contextualizes the raw data, generating a risk score and recommending concrete remediation steps—such as upgrading to a patched version or replacing an insecure dependency with an alternative.

Key capabilities include:

• Docker‑friendly deployment that supports HTTP streaming, eliminating the need for SSE and ensuring low latency interactions.
• GitHub integration that scans public or private repositories up to 1 GB, making continuous security monitoring a natural part of CI/CD pipelines.
• Secrets detection that scans code for exposed API keys, passwords, or other credentials, a common source of breaches.
• Dockerfile analysis to surface vulnerable Python packages before container images are built.
• Smart caching that stores scan results per commit, dramatically reducing repeat work in large repositories.
• Automatic space management that caps disk usage at 2 GB, preventing runaway resource consumption in long‑running environments.

In practice, a development team can embed VulniCheck into its existing AI workflow: a code review assistant triggers during pull requests, while the same AI model can answer questions like “What CVE does this package have?” via . For operations teams, the tool provides an AI‑generated risk overview that can be reviewed before deploying new releases. Because the server is zero‑config by default and optionally enriches its analysis with OpenAI, Anthropic, or GitHub tokens, it scales from a single developer’s machine to production‑grade deployments with minimal friction.

Overall, VulniCheck offers developers a powerful, AI‑enhanced security companion that streamlines vulnerability discovery, simplifies remediation planning, and integrates seamlessly into existing MCP‑based AI workflows.