"ANA" matching MCP servers:

An intelligent MCP toolset for software testers that monitors code changes, analyzes test impact, recommends tests, and assesses risk, supporting multiple AI coding frameworks via dual transport modes.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Windows桌面截图与窗口自动化工具，支持后台操作、不抢焦点，可截取窗口/屏幕、模拟鼠标键盘、点击菜单等。

Enables to manage RGAA accessibility audits on the Ara platform via MCP-compatible clients like Claude Code or Codex, supporting full CRUD operations and audit lifecycle without manual login.

Enables AI-driven analysis of SAP transport requests, including metadata retrieval, code diff analysis, and risk detection for ABAP objects.

Enables AI coding tools to query and validate analytics event specifications maintained as Wiki markdown tables, providing structured access to events, properties, and implementation details through MCP.

Provides tools to analyze test failures, cluster similar failures, and detect flaky tests from input or log files, helping QA teams debug and triage issues.

Enables AI-assisted analysis of log files through advanced searching, filtering, and test execution capabilities. Supports time-based queries, pattern matching, test summarization, and code coverage reporting directly within compatible MCP clients.

Enables AI agents to control Copper Mountain Vector Network Analyzers over TCP/IP SCPI, offering 45 tools for sweep configuration, calibration, measurement, and Touchstone export.

Enables backend test quality analysis for GPA e-commerce, including coverage analysis, reverse engineering, architecture mapping, and test suite generation.

Enables comprehensive analysis of JavaScript/TypeScript project testing setups by detecting frameworks like Jest, Vitest, and Cypress, analyzing test coverage metrics, and generating actionable recommendations for improving test quality. Provides detailed insights into test structure, dependencies, and coverage thresholds with visual feedback.

Enables AI coding agents to test C++ code by running static analysis, sanitizers (ASan, TSan, LSan, UBSan), and performance profiling through eight MCP tools.

Bridges AI models with WinDbg to analyze Windows crash dumps and perform remote debugging through natural language queries, enabling execution of debugger commands and automated crash analysis.

Maps Electron app IPC handlers to MCP tools and provides 33 built-in CDP tools for DOM automation, screenshots, interaction, and debugging, enabling Claude Code to directly control your Electron app.

A Model Context Protocol (MCP) server for automated crash analysis using GDB.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Provides comprehensive code analysis through three MCP servers: static analysis for code quality and security, dependency analysis for package management and vulnerabilities, and complexity analysis for maintainability assessment across multiple programming languages.

Analyzes app screenshots to identify UI/UX issues, compare designs with implementations, and provide actionable fixes using GPT-4o/GPT-5.2 vision capabilities. Supports single/batch analysis, design comparison, and automated report generation for iOS, Android, web, and desktop platforms.

Enables AI-driven triage of Allure test results by clustering failures into stable signatures and generating root-cause explanations, so you can turn large failing CI runs into a few actionable categories.

An MCP server that analyzes SQL schemas, generates realistic test data, benchmarks queries, and provides optimization recommendations to improve database performance.