Enables coding agents to request, track, and verify independent cross-model reviews with structured findings, attachment hashes, and provenance receipts via Chrome Bridge, Codex, or Responses transports.
MITM proxy MCP server that intercepts and verifies AI agent tool calls, detecting fabrication, tracking costs, verifying outcomes, and inferring user satisfaction.
Enables comprehensive control of iOS simulators and real devices through AI assistants, supporting app management, UI automation, screenshots, media operations, and location simulation for iOS development and testing workflows.
MCP server for automatic Storybook story generation, component analysis, and validation. Auto-detects React frameworks and syncs component documentation.
Validates Mermaid diagrams with comprehensive grammar parsing supporting 28+ diagram types. Processes markdown files, ZIP archives, and direct input with detailed error reporting and enterprise-grade performance capabilities.
Exposes a set of CLI tools (test generation, documentation generation, linting, test running, code search) to AI assistants via MCP, allowing them to perform these tasks through natural language.
AI-controlled browser farm — run N isolated Chromium instances with 36 MCP tools for device emulation, screenshots, network/geolocation simulation, and diagnostics. Cross-platform: Windows GUI + headed/headless on Linux/macOS.
Read visual bug reports from Feedbug in your AI coding agent: screenshot, console logs, failed network requests, session replay and the DOM context of the element the tester clicked. Trace the bug to its source file, comment and resolve without leaving the editor.
AI 規格大師 — MCP server bridging specs (Linear / JIRA / GitHub Issues / Notion / Markdown / Figma) to tests, with bidirectional traceability and a spec-quality coach. Sibling to mk-qa-master.
Local MCP tool for understanding JSON Schema files while planning tests, summarizing types, required fields, constraints, and generating placeholder paths without copying data.
Enables natural language-driven creation and execution of autonomous-vehicle scenarios in the CARLA simulator, with validated primitives and replay support.
Enables LLMs to control RIGOL oscilloscopes over SCPI via LAN or USB, including configuring channels/timebase/trigger, taking measurements, capturing waveforms and screenshots, decoding serial protocols, running FFT analysis, and controlling the built-in AFG.
Enables comprehensive Roku automated testing by combining WebDriver for UI state verification and ECP for device control, allowing validation of acceptance criteria through natural language queries.