"A platform for collaborative design and prototyping (Figma)" matching MCP servers:

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

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

MCP server for operating and testing Android devices with AI agents. Provides observation and action tools for interactive control and spreadsheet-driven test runs.

Enables agents to convert Figma frames into self-contained HTML and then iteratively verify the rendered code in Chromium against the design using pixel diffs and element bounding-box checks until it converges.

Enables AI coding agents to audit rendered web pages for design and UI defects by measuring geometry, styles, contrast and motion in a local browser and judging them against evidence-tiered rules. Findings come back as short, fixable items expressed in the project's own design tokens, while only sanitized numeric measurements leave the machine.

Enables agents to compare a live web page against a Figma frame and receive a bounded JSON verdict of design deviations, without sending screenshots or mockups through the model context.

Compare design and implementation screenshots using pixel-by-pixel analysis, generating visual diff images and metrics.

Enables Claude Code to read and write issues on the Testing Platform via natural language, including listing projects, filtering issues, creating and updating issues, and adding comments.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

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

An MCP server that measures how faithfully one UI reproduces another, returning a score and actionable findings for improvement.

Enables creating, validating, and simulating Crack-style story chat through a unified MCP server, with template and keyword management, automated project audits, session play, and persona-separated testing to prevent omniscience.

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

Automates the conversion of Figma designs into TypeScript React components and integrates with GitHub to create pull requests for the generated code. It includes visual regression testing with Playwright and accessibility validation to ensure implementations match the original designs.

Enables AI agents to interact with 1C: Enterprise development environment, including running tests, managing launch profiles, building configurations, and performing database operations through the 1C: Platform Tools extension.

Enables users to run synthetic support-agent evaluations, preserve and inspect traces, score results deterministically, inject failures, and enforce release review gates through MCP tools, resources, and prompts.

Enables AI agents to validate design system constraints, including WCAG accessibility, token relationships, and consistency, through MCP tools like validate, explain, and suggest-fix.

MCP-first A/B testing server that enables agents to manage experiments (create, update traffic splits, read results, apply winning variants) from tools like Claude Code, with self-hosted Cloudflare backend.

Exports Figma designs as ready-to-use HTML/CSS and validates pixel-perfect accuracy with image comparison and visual regression testing.

Enables LLMs to interact directly with generated REST services by discovering and invoking CRUD tools and reading exposed resources for list and get-by-id data.

An LLM-driven API testing platform exposed over MCP, enabling declarative test definition and DAG-based execution.

Enables coding agents to compile supplied files, run optional focused tests, and get static findings across ten programming languages, providing a deterministic feedback loop on the code they are about to return.

Voice-powered bug reporting MCP server with 13 tools. Captures console logs, network errors, DOM traces, and developer voice descriptions. AI reads structured reports to find and fix bugs automatically. Chrome Extension + Python CLI.

Zero-sidecar MCP server embedded directly in the Godot 4 editor as a GDScript EditorPlugin — no Node or Python sidecar. Reflection-driven tools over any engine class, validate-before-write GDScript, scene/script/resource authoring, and a runtime play-test loop (play, screenshot, input, assert).

Verifies AI agent completion claims by checking that claimed output files exist and are fresh, acting as a lightweight physical telemetry gate.

Validates .mcp.json and claude_desktop_config files to catch duplicate server names, missing binaries, invalid environment variables, and unreachable endpoints. Reports each problem along with how to fix it, so silently failing MCP servers can be diagnosed and repaired.

Enables users to query, create, update, and manage Digital.ai Agility assets through MCP tools, with secure Windows Credential Manager authentication and configurable write operations.

Enables AI assistants to operate directly inside a running Unity Editor project, creating scenes, generating scripts, simulating input, capturing screenshots, inspecting performance, and automating editor tasks through its built-in tools and resources.

Enables MCP clients to retrieve lab performance scores and field Core Web Vitals data for web pages via Google APIs.

Generates a pytest suite from acceptance criteria withheld from the coding agent, so the tests can disagree with the code; qikly_run never returns the criteria. The CLI also scores an existing suite by planting faults, and ships as an agent Skill (qikly --install-skill). Runs locally (uvx --from "qikly[mcp]" qikly-mcp): your code and API key stay on your machine.