ALT Decision Tree MCP Server

The ALT Decision Tree MCP Server fills a critical gap in AI‑powered web accessibility by automating the generation of high‑quality alternative text for images. Traditional methods rely on manual tagging or generic OCR, which often miss contextual nuance and fail to adhere to W3C WAI‑ARIA guidelines. This server implements a structured decision tree that evaluates an image’s purpose, content, and complexity to produce concise, meaningful alt text, complete with a confidence score that lets developers gauge reliability.

At its core, the server exposes two primary tools. The generate_alt_text tool accepts a base64‑encoded image and optional contextual metadata, then returns the alt text, a brief reasoning trace, the decision‑tree path taken, and a confidence value between 0 and 1. This transparency is invaluable when debugging or refining AI workflows, as developers can see why a particular description was chosen. The get_alt_guidance tool supplies best‑practice guidance and explains the decision tree logic, helping teams align their usage with accessibility standards.

Key capabilities include:

• Context‑aware analysis: The server considers the image’s intended use—decorative, informational, or functional—to decide whether to provide an empty alt string or a descriptive phrase.
• Text extraction: When images contain embedded text, the server surfaces it directly as alt text, preserving important information.
• Complexity handling: Graphs, charts, and detailed illustrations receive a concise summary plus an optional extended description, striking a balance between brevity and informativeness.
• Confidence scoring: Each output is accompanied by a numeric confidence level, allowing downstream systems to decide whether human review is needed.

Real‑world scenarios where this MCP shines include content management systems that automatically tag uploaded media, e‑commerce platforms ensuring product images are accessible, and AI assistants that need to describe visual content in natural language responses. By integrating the server into existing MCP‑enabled workflows, developers can offload repetitive alt‑text creation to a rule‑based model that remains compliant with evolving accessibility standards.

Unique advantages of the ALT Decision Tree MCP Server lie in its explicit decision‑tree structure, which provides explainability—a feature often missing from black‑box image captioning models. Coupled with the confidence metric and guidance tool, it empowers developers to build trustworthy, standards‑compliant AI applications without sacrificing speed or scalability.