"How to work with Claude AI code or integrate it" matching MCP servers:

Enables AI-powered automated testing, security scanning, code review, and maintenance tasks directly within Claude Code or desktop.

Enables MCP-compatible agents to audit an entire repository through one question, detecting stacks and running parallel checks for dead code, dependencies, security, CVEs, complexity, and lint across Laravel/PHP, Python, TypeScript/React, and Flutter/Dart. It returns normalized, severity-sorted findings with false positives flagged and secret values kept out of the output.

An AI-native code security audit MCP server that supports multi-language AST analysis, call graph analysis, vulnerability detection, and AI deep audit.

Enables automated security code auditing using LLM and MCP, including AST parsing, taint analysis, dataflow tracing, and automated PoC generation for multi-language codebases.

Multi-language SAST and AI-powered fuzzing MCP server for Claude Code integration, enabling static and dynamic security analysis of code.

Enables AI coding assistants to run static analysis on GitHub repositories, raw code snippets, or local directories to detect cloud cost leaks in Terraform/AWS configs, PostgreSQL migration table-lock hazards, and OWASP/Next.js API security gaps. Each finding returns structured severity, line-level detail, and drop-in fixes.

Enables security testing of MCP guardrails and SAST scanners by exposing intentionally vulnerable code patterns that are inert by default and make no network calls.

MCP server that guides LLM security reviews step-by-step, tracking progress and ensuring all assigned source code is read before completing each plan step.

Enables coding agents and CI/CD pipelines to audit untrusted code by detecting Python sandbox escapes, SQL injection taint, ReDoS regex vulnerabilities, and CORS misconfigurations, while generating Seccomp BPF filters for safer execution.

Provides 28 MCP tools across 16 analysis engines for comprehensive Python code quality assessment, including complexity scoring, security scanning, dead code detection, dependency auditing, and test quality analysis.

MCP server for AI-driven VAPT orchestration, enabling agents to plan and execute authorized security scans through a control plane that enforces scope, sanitization, budget, rate limits, human approval, and audit logging.

An authorization-first MCP-governed control plane for executing security-tool workflows in explicitly approved, sandboxed environments with auditable campaigns and AI collaboration boundaries.

Enables AI agents to autonomously run over 150 cybersecurity tools for penetration testing, vulnerability discovery, bug bounty automation, and security research through MCP.

Connects Claude to AI/ML evaluation harnesses, token latency profilers, and prompt security red-teaming tools, enabling model benchmarking, latency profiling, and security assessment through MCP tools.

Enables AI agents to plan and run authorized offensive security assessments across 150+ integrated tools, with full activity logging of every scan.

Enables AI coding agents to audit websites for PHP runtime vulnerabilities, compile a live Zero-PHP Next.js App Router replacement preview from a WordPress/PHP site, and retrieve remediation guidance.

Autonomous pentests from one command: real security tools, working PoCs, and audit-ready reports, all driven via MCP.

Enables Claude Code to leverage OpenAI models for expert code review, deep architecture analysis, and security audits, with automatic markdown report generation.

Enables natural-language-driven security testing by orchestrating multiple pen-testing tools through MCP, with automated scan execution and AI-assisted vulnerability summarization.

Offers a control surface for AI agent security with four independent checks: source-code scanning, tenant isolation, LLM content verification, and deploy gating. Currently in pre-release, with no functional scanning engines yet.