Argus – Repository Analysis and Security Assessment Tool

Argus is an MCP (Model Context Protocol) server designed to give AI assistants instant, deep insight into software repositories. By exposing a set of rich analysis tools over the MCP interface, it removes the need for developers to manually run static‑analysis or vulnerability scanners on every branch. Instead, an assistant can query Argus for a concise report that covers code quality, potential security risks, and structural changes—all in a single request.

The server tackles several pain points that developers face when reviewing code. First, it automates the tedious process of language detection and tool selection; whether a project is written in Go, Java, Python, or TypeScript, Argus automatically chooses the appropriate linters (gocyclo, golangci‑lint, PMD, Pylint, Bandit, ESLint). Second, it consolidates security scanning by integrating Trivy, providing a unified vulnerability report that spans all supported languages. Third, it offers git‑centric operations—branch enumeration, commit history analysis, and diff comparisons—so an assistant can surface the exact changes that introduced a bug or vulnerability. This combination of static analysis, security scanning, and git introspection is rarely found in a single toolchain.

Key capabilities include:

• Multi‑language static analysis with automatic detection and tooling per language.
• Comprehensive security scanning through Trivy, producing a single vulnerability report for any branch or commit.
• Git operations such as branch listing, history traversal, and diff comparison, enabling contextual insights into code evolution.
• Repository structure visualization that helps assistants explain the layout of large monorepos or multi‑module projects.
• Graceful error handling where tool failures are reported as warnings rather than crashes, ensuring the assistant can still provide useful feedback.

Real‑world scenarios that benefit from Argus are plentiful. A CI/CD pipeline can query Argus to enforce quality gates before merging a feature branch, automatically rejecting code that exceeds complexity thresholds or introduces new vulnerabilities. An AI‑powered code review assistant can ask Argus for a quick “what changed in this PR?” summary, complete with linting errors and security findings. Security teams can schedule periodic scans across multiple repositories and receive a unified report that highlights new high‑severity findings.

Integrating Argus into an AI workflow is straightforward: the assistant sends a standard MCP command (e.g., or ) with the repository URL and optional branch. Argus returns a structured response containing tool outputs, summaries, and actionable insights. Because the server is built on top of MCP, it can be paired with any AI platform that supports the protocol, from Claude to GPT‑based assistants, making it a versatile bridge between source code and conversational AI.