"Using AI to Generate XUI Content on a Whiteboard" matching MCP servers:

Enables MCP-capable agents to read, create, modify, align, distribute, organize and export local-first infinite whiteboard content, including sticky notes, text, shapes, arrows, ink, mind maps, images, files and links across multiple pages. Provides 16 tools over stdio or Streamable HTTP, plus resources and prompts, with version-conflict protection and audit trails so canvas edits never silently overwrite newer content.

Enables detection of AI-generated content from ChatGPT, GPT-4, and Gemini models through the BACH AI Content Detector API.

MCP server for generating and editing images and videos via DashScope/OpenAI-compatible APIs, with tools for text-to-image, image editing, text/video generation, and automatic local download.

Visual tech-stack inventory of any codebase: languages, frameworks, databases, AI SDKs, infra.

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

Extract content from URLs, documents, videos, and audio files using intelligent auto-engine selection. Supports web pages, PDFs, Word docs, YouTube transcripts, and more with structured JSON responses.

Stop your AI agent from rebuilding what already exists. MCP server that checks GitHub, npm and Python repos for maintained alternatives before you scaffold.

Thin HTTP wrapper for the Generate-Data.com API, enabling data generation, schema proposal, and field type listing through MCP tools.

MCP server that manages in-memory SVG path canvases and provides preview URLs for rendering in a sandbox iframe via AIRI's surface contract.

Provides an MCP server that tracks open loops (pending decisions or replies) across email, agent sessions, and other channels, enabling agents to query what is waiting on whom.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

AutomateLab Content Distribution publishes content across DEV.to, Hashnode, GitHub Discussions, Reddit and Bluesky, with browser workflows for LinkedIn, Medium and X. Tracks publishing state and adapts content per platform. Product and documentation: https://automatelab.tech/products/mcp/content-distribution-mcp/

Provides AI agents with structured short-form content mechanics including hooks, script structures, retention strategies, and CTAs, along with auditing tools to avoid common posting failures.

Enables crawling websites, including JavaScript-rendered pages, and returns extracted page content as JSON with structured error reporting.

SERP-based content scoring and optimization with 7 SEO categories.

Analyzes YouTube videos and X (Twitter) feeds to extract transcripts, generate takeaways, TLDRs, social hooks, and comic-style infographics, and syncs results to GitHub, all callable as MCP tools.

Enables editorial content QA over the Model Context Protocol with four local tools that analyze readability, AI-sounding language, SEO on-page factors, and produce full reports where every finding includes an actionable fix.

Provides content agents with structured search over editorial guidelines, visual templates, and past post outcomes, enabling auditable proposals that cite rule IDs.

Enables interaction with Prismic through tools for reading documents, managing media assets, and performing content migrations or updates. Supports the Prismic Content, Asset, Migration, and Custom Types APIs with secure write operations and multi-transport support.

AI-powered MCP server for file renaming, organization, and PDF splitting using natural language prompts and content analysis.