"OWASP Dependency-Check" matching MCP servers:

Enables scanning repositories for hardcoded service URLs and clients, and building service-to-service API dependency maps.

Enables AI coding agents to list, read, create, update, delete, and render PlantUML and Mermaid diagrams, and check them against the actual codebase to detect architecture drift.

Offline MCP server for SAP ABAP static analysis, ABAP Cloud readiness scoring, and RAP scaffolding. It enables AI agents to lint ABAP source, compare reworks, check released APIs, and generate RAP business objects without needing an SAP system.

MCP server that provides a live, terminal-native layered dependency map for AI coding assistants, with tools to track and visualize development progress in real time.

Analyzes a local codebase and exposes its structure, per-file summaries, dependency graph, and complexity hotspots as tools for MCP clients like Claude.

An MCP server that exposes a Svelte/SvelteKit project's component dependency graph over stdio, enabling AI coding assistants to query component imports, unused components, and route dependencies without an editor.

Provides read-only structural analysis of codebases, surfacing architecture smells with traffic-light ratings and rendering dependency relationships as Mermaid flowcharts.

MCP server for repository mapping, dependency analysis, and architecture diagram generation for JavaScript, TypeScript, and Python projects.

Cross-repository code knowledge graph MCP server for Java, Kotlin, JavaScript, and TypeScript. Indexes source code into embedded KuzuDB via tree-sitter and exposes 30+ tools for call-flow tracing, multi-hop taint analysis (OWASP/CWE/PCI/STIG), entry-point reachability filtering, performance hotspot detection, and license compliance — without reading source files. 95% fewer tokens vs source-read

Analyzes software projects to extract architecture, build dependency graphs, and predict the impact of code changes.

Enables AI to analyze, query, and manage a graph-based representation of software architecture for impact analysis, dependency tracking, and design.

Automatically generates Architecture Decision Records (ADRs) from Claude Code conversations using Claude Opus. Features AI quality review, duplicate detection, and dependency graph.

An AI-powered dependency decision engine that lets agents analyze blast radius, simulate upgrades, trace security attack paths, and visualize dependency topology in a 3D galaxy viewer.

Provides AI agents with a live architecture model of a codebase, enabling queries for root cause analysis, blast radius, and dependency traversal through MCP tools.

Transforms codebases into structural knowledge graphs for AI agents and developers, providing precise architectural awareness and dependency mapping.

Analyzes GitHub and local repositories to automatically generate visual architectural diagrams such as dependency graphs, class diagrams, and data flow diagrams.

Enables AI agents to record and retrieve architectural decision records, check precedent before making decisions, and avoid repeating past mistakes or contradicting settled choices.

MCP server for rendering Mermaid diagrams and generating source-code diagrams, with tools to render text/files and produce dependency/class diagrams.

MCP server that provides coding agents with structured repository context, including graph-based navigation, dependency analysis, runtime flow tracing, and configuration surface across supported stacks.

A Model Context Protocol server for deep codebase understanding of Python projects, focusing on data analysis and scientific computing. It provides architectural analysis, pattern detection, dependency mapping, test coverage analysis, and AI-optimized context generation.