"An open-source penetration testing framework" matching MCP servers:

Enables researching, verifying, comparing, and composing open-source AI projects with transparent evidence and uncertainty boundaries through read-only tools.

Code-aware browser testing agent — reads your codebase, understands functionality, tests every element, reports with screenshots. Works as MCP server for Cursor/Claude Code or standalone CLI.

Provides access to over 40 industry-standard penetration testing tools, including Nmap, SQLMap, and Metasploit, within an isolated Kali Linux Docker container. It enables security professionals to perform comprehensive network reconnaissance, web application testing, and vulnerability research through natural language commands.

Exposes structured Microsoft Dynamics 365 Business Central source code (versions 23-29, 47 localizations) to AI agents via MCP, enabling fast lookups, searches, and code analysis.

Enables AI assistants to write and debug integration tests by providing live access to DOM structure and executing code directly in test environments.

MCP server for testing GitHub Actions workflows locally using nektos/act.

Enables accessibility testing of websites and HTML content using axe-core and IBM Equal Access engines. Supports WCAG compliance checking, multi-viewport testing, and provides detailed violation reports with remediation guidance.

MCP server for searching, browsing, and analyzing decompiled Minecraft source code locally. Supports symbol lookup, text search, reference lookup, and lightweight RAG.

An MCP server that enables AI assistants to navigate, search, and analyze local Keycloak source code to support developer customizations like SPIs and authenticators. It provides tools for searching classes, generating boilerplate code, detecting breaking changes between versions, and tracing dependencies.

Enables automated testing and coverage reporting for MCP services with test execution, file generation, and mock service creation. Provides comprehensive testing infrastructure including Jest integration, coverage reports, and health checks for the MCP ecosystem.

MCP server that enables AI agents to programmatically run tests, query results, and receive intelligent recommendations about test execution strategy.

Open Kioku is the local-first code intelligence and evidence layer for AI coding agents (Claude, Cursor, Codex, Zed, and other MCP clients). Instead of letting agents blindly crawl files, guess blast radius, or read the entire repository repeatedly, Open Kioku gives them a precise, pre-indexed code index to build evidence-backed plans before they edit, and verify boundary limits after they edit.

Local-first memory layer for AI coding agents — Markdown as source of truth, MCP server + CLI, human-reviewed capture, team knowledge via git PRs

A lightweight, local-first MCP server for executing HTTP requests and managing API collections and environments without cloud dependencies. It enables testing APIs, handling authentication, and importing OpenAPI specifications directly within MCP-compatible workflows.

Provides on-demand access to AOSP and Jetpack source code to help AI understand Android framework internals. It features a hybrid architecture using Tree-sitter and LSP for precise code extraction, cross-file navigation, and local source indexing.

Enables revision-bound source audits with exact article fingerprinting, claim-to-source mapping, quotation verification, and immutable JSON evidence reports for prepublication review.

An MCP server that executes tox commands to run Python tests within a project using pytest, allowing users to run all tests or specific test groups, files, cases, or directories.

Analyzes source code dependencies across multiple programming languages in the specified directory to identify file relationships, assisting in dependency management and project structure understanding.

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

Enables reproducible evaluation of AI agents on multi-source evidence investigation tasks, providing Docker-based scenarios, MCP tools, and automated scoring for payout reconciliation, performance-claim verification, and incident reconstruction.