"QA review sentinel test marker" matching MCP servers:

Local MCP server for collecting visual review feedback from a live local HTTP preview target. It serves a browser Review UI, stores region annotations, and exposes finalized review sessions to agents through MCP tools.

Exposes surface-bound annotations placed on a Blender model to AI agents, letting them read pending change requests with stable IDs, revisions, positions and visibility, then submit completion summaries by marker ID after editing. This enables batch, review-oriented workflows where the user approves or rejects each agent change in Blender, without requiring object selection, screenshots, or a fixed port bridge.

Enables an AI assistant to open 3D model files (STL, 3MF, Bambu Studio and OrcaSlicer projects) in a local browser viewer where the user clicks exact spots to leave feedback. Each note comes back with the part name, millimetre coordinates, surface direction and a numbered screenshot, so the assistant can revise the design and show the new version.

An MCP server that auto-generates presentations from a topic using AI, supporting slide editing, visual QA, and export to HTML/PPTX.

Compares Figma frames to live pages, checking colors, fonts, and border radii, and generates a shareable HTML report.

Provides UI architecture analysis for converting Figma designs or web pages into reusable UI code blocks with effort estimation in CSV format.

An MCP server connecting Claude to a running Archicad 29 instance for delivery-readiness QA via YAML rules and full Archicad API access, including querying, editing, creating elements, and schedule scheme editing.

Orchestrates full-stack frontend engineering—from intent discovery and design genome generation to automated multi-viewport QA—through JSON-RPC tools for AI agents.

Provides 8 MCP tools for frontend design: load design system, generate components, scaffold pages, review consistency, extract tokens, check WCAG 2.2 AA, test responsiveness, and export to Figma.

Enables local generation and validation of Considerate Pouchers one-pager deliverables with enforced brand guidelines, contextual visuals, layout QA, and strict file validation.

Enables MCP clients to drive native CAD and 3D applications—Autodesk Fusion, Blender, and Unreal Engine—by planning work, executing trusted Python, inspecting real geometry, revising editable source files, and handing assets between applications. Each job keeps durable state, revision checks, and artifact hashes, with role-based handoffs for planning, modeling, geometry QA, interchange, and review.

Enables AI assistants to read .xd design files locally in a sandboxed, read-only way, extracting artboards, layers, text, colors, fonts, previews, assets, components, design tokens, and prototype flows for design-to-code, Figma recreation, QA, and documentation.

Enables AI assistants to control Adobe After Effects for composition, layer, effect, preset, marker, and audio automation through a bridge panel.

Enables MCP-capable assistants to inspect, create, measure, and review Blender production workflows for characters, rigs, animation, game meshes, facial performance, secondary motion, and PBR materials with checkpoints and visual QA.

Enables Claude to run automated Figma-to-website design QA scans, including page discovery, visual regression and UI comparison, and structured severity-ranked reports, all within a conversation.

This MCP server renders UI design artifacts headlessly, runs deterministic linters, and manages stateful design review loops with an independent vision critic.

MCP server that exposes a design system and UX rulebook as tools, enabling AI to generate UI consistent with your design tokens, components, layout rules, accessibility guidelines, and templates. It also provides a review tool to score UI proposals against the design system.

Enables AI apps to create and edit real HTML/CSS designs on a live multi-device canvas, manage projects and assets, collect element-pinned feedback, run automated QA, and export clean code through MCP tools.

Enables AI coding agents to plan, build, and review websites and product interfaces with a persistent, user-led process, including design direction, component contracts, and implementation review.

Lets AI clients draft, inspect, edit and review wireframe/storyboard boards over MCP, sharing one live board with the app the user has open. It also supports loading boards back, resolving review notes, and exporting realistic previews so specs can be iterated from natural language.