"Firecrawl MCP (Model Context Protocol) Integration" matching MCP servers:

Enables AI clients to query, aggregate, and visually inspect a live Autodesk Revit model, reading it without model-changing transactions by default. Model mutations are opt-in and require two explicit gates, with dry-run previews and verification.

A Model Context Protocol (MCP) server that enhances Figma design integration by adding element position information to API responses, enabling AI assistants to better understand spatial relationships and convert designs to code more accurately.

Converts Figma designs into structured code context with token-aware styling, enabling AI agents to generate production-level frontend code.

An MCP server that gives AI coding tools access to Figma design data for accurate one-shot implementation of designs.

Converts Lanhu design URLs into AI-ready implementation context including HTML+CSS (Tailwind), image downloads, design tokens, and guidance.

Open, secure runtime that puts Revit, Rhino, Navisworks, and Power BI behind one MCP call center, enabling an AI agent to orchestrate them directly instead of manually dumping data between disconnected apps.

Loads your project's Tailwind CSS config and exposes tools for AI agents to query actual design tokens and validate class names, preventing hallucination of non-existent utilities.

Integrates PTC Windchill and Creo Parametric with LLM-based clients via the Model Context Protocol, enabling natural language interaction with PLM and CAD systems for tasks like part search, BOM retrieval, model operations, and exports.

An MCP server that gives AI coding agents access to Figma design data, enabling pixel-perfect implementation of designs through structured data extraction, image downloads, and design token retrieval.

Exposes tools to ingest FigJam boards, retrieve context summaries, and answer free-form questions about the board content.

Applies Jungian shadow complement to unified composition parameters, creating psychological depth through systematic visual opposition after multi-domain blending.

Converts Figma design data into structured semantic analysis JSON and screenshots for AI agents to use in design-to-code workflows.

Enables AI agents to extract design systems, analyze components, and maintain design-code consistency from Figma files, providing intelligent component analysis and accessibility compliance.

Model Context Protocol integration for the Figma API, enabling AI applications to access Figma files, components, and design tokens.

Enables coding agents like Cursor to access Figma design data, fetch layout and styling information, and implement designs in any framework.

This repository contains an implementation of an Miro Universal MCP (Model Context Protocol) server. It provides a standardized interface for interacting with Miro's tools and services through a unified API.

Enables Cursor to access Figma design data via MCP, allowing it to fetch simplified layout and styling information to accurately implement designs from Figma files.

Enables searching, indexing, linking, and downloading 2000+ open-source fonts from fontsource.org, with support for font subsets, weights, styles, variable fonts, and CSS integration.

Enables live preview of Storybook components inside the IDE as the AI edits them, using MCP Apps integration.

Enables AI clients to control a running Blender instance through the Model Context Protocol, allowing scene inspection, object creation and editing, rendering, and execution of Blender Python. It works locally via a WebSocket bridge between the MCP server and a Blender add-on.