Vision QA MCP

An MCP server that gives an AI agent automated quality control for generated images. After a model produces an image, the agent calls qa_check and gets back a structured verdict — character accuracy, style consistency, quality, and composition, scored against your reference images and rules — powered by Claude vision.

Built for AI media pipelines where a human can't eyeball every frame: generate → QA → regenerate-if-failed, automatically.

Why

Image models drift. A character loses a feature, the style wobbles, a hand comes out wrong. In a production pipeline that ships dozens of images, you need the agent itself to catch this before a human ever sees it. This server makes "QA every image" a single tool call with a pass/fail and actionable issues — and it pairs naturally with claude-vision-mcp (let the agent see) for a full see-and-verify loop.

Tools

• qa_check — review one image and return a structured verdict:
  • passed (bool), overall_score, and per-axis scores (character_accuracy, style_consistency, quality_score, composition_score) on a 0–1 scale
  • issues — a list of {severity, category, description, recommendation} (severity: critical / warning / minor)
  • should_regenerate (bool) and a one-line notes summary
  • Fails automatically on any critical issue, regardless of score.
• list_scene_types — the supported scene_type values and what each expects.

You pass your own rules and references, so it works for any project:

qa_check(
  image_path="/path/to/generated.png",
  reference_images=["/refs/hero_front.png", "/refs/hero_face.png"],
  character_rules="The pilot has NO eyebrows in this form. Jacket has horizontal stripes, not clouds.",
  style_notes="High-contrast anime cel shading, cosmic purple lighting.",
  scene_type="solo",          # solo | portrait | battle | combat | group | action | interview
  pass_threshold=0.7,
)

scene_type adjusts composition expectations — e.g. a portrait may face the camera, while battle characters should face each other.

Requirements

• Python ≥ 3.10
• An Anthropic API key (ANTHROPIC_API_KEY)
• Dependencies: mcp[cli], anthropic, Pillow

Install

git clone https://github.com/wonderstone843/vision-qa-mcp.git
cd vision-qa-mcp
pip install -e .
export ANTHROPIC_API_KEY=sk-ant-...

Use with Claude Code

claude mcp add vision-qa -- vision-qa-mcp

Or add to your MCP config:

{
  "mcpServers": {
    "vision-qa": { "command": "vision-qa-mcp" }
  }
}

Then instruct your agent: "After generating each image, run qa_check against the character refs; regenerate any that don't pass."

Configuration

• ANTHROPIC_API_KEY (required)
• ANTHROPIC_MODEL (optional, default claude-opus-4-8) — for QA on every generation, claude-haiku-4-5 or claude-sonnet-4-6 are cheaper and usually sufficient.

How it works

The image is downscaled to stay under the vision API limits, sent to Claude alongside any reference images and your rules, and the model is forced to call a submit_qa tool whose schema defines the four scores plus the issues list — so the output is always structured and parseable. The pass decision is overall_score >= pass_threshold AND no critical issues.

vision_qa_mcp/
  server.py   FastMCP server: qa_check + list_scene_types
  review.py   prompt, scoring rubric, forced-tool call to Claude vision
  images.py   downscale + base64-encode for the vision API

License

MIT — see LICENSE. Author: Joshua Penn.