A continuous, out-of-band trust and reliability layer for the MCP ecosystem. It fingerprints MCP server tool definitions, detects and classifies drift (e.g., rug pulls) via a severity taxonomy, maintains a hash-chained evidence ledger, and gates CI with SARIF—while also acting as an MCP server itself so agents can check a server's safety before binding.
MCP server that provides audit and safety-check tools for enterprise SDLC code integrity, enabling AI agents to scan workspaces for lifecycle gaps, mock-theater tests, DRY violations, and language-specific issues in shell, JavaScript/HTML, and Python.
MCP server that builds a dependency graph of your codebase, giving AI agents structural understanding to analyze impact, find context, and make safe modifications across languages and infrastructure.
MCP server that scans PyPI packages and GitHub repos for security vulnerabilities, dangerous patterns, and prompt injection vectors, providing trust scores (0-10) and side-by-side comparisons.