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Automatically QA generated images against references and rules with structured scoring.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Vision QA MCP" yet — see the docs or source repo.
Compare this batch of generated e-commerce hero images against the brand reference images and QA them using these rules: logo must not be distorted, main product must be centered, background must be pure white, and no extra text may appear. Return an overall score, category scores, failure reasons, and fix suggestions for each image.
Structured QA results for each hero image, including scores, violations, and revision suggestions.
Run automated validation on 50 ad creatives using the sample images as the visual style baseline. Check whether text areas have enough whitespace, people are fully visible, and brand colors are consistent. Output JSON results suitable for filtering approved creatives.
A program-friendly JSON validation report that clearly separates passing and failing creatives.
We updated our image generation model. Evaluate the outputs from both the old and new versions against the reference images, focusing on composition consistency, missing key elements, text readability, and color deviation. Provide a quality comparison conclusion between versions.
A comparative QA report for the old and new models, highlighting improvements, regressions, and the overall conclusion.
Run visual regression tests and review structured failure reports through MCP clients.
MCP tool for QA workflows, testing, defect tracking, and reporting.
Analyze local or remote images with vision LLMs and generate descriptions.
Run local QA checks for APIs, test cases, errors, and SLA evaluation.
Enforce configurable QA strategies so AI-generated tests meet strict standards.
Analyze images for AI-generation signs using multiple forensic inspection methods.