The gap between having an AI application idea and successfully deploying one has never been wider. Modern development requires orchestrating databases, APIs, authentication, deployment pipelines, and infrastructure—all before you can even see if your idea works. For developers and early-stage startups, this complexity creates a frustrating bottleneck between innovation and execution.
Today, we’re launching the Public Beta of Raindrop, the first Claude Native Infrastructure Platform that eliminates this friction entirely. By connecting to Claude Code through the Model Context Protocol (MCP), Raindrop transforms AI-assisted development by moving beyond code snippets to building complete deployed applications.
Every developer knows the frustration. You have a brilliant idea for an AI application—maybe a document processing service, an intelligent chatbot, or a recommendation engine. But between your idea and a working application lies a maze of infrastructure decisions:
These challenges are not unique—they are common across the tech industry, affecting teams of all sizes and experience levels.
Most developers end up spending 80% of their time on infrastructure and only 20% on the actual application logic that makes their idea unique. This creates significant limits on innovation and product development. Early-stage startups often hire entire backend and DevOps teams just to handle these concerns, burning through runway before they can validate their core concept.
Raindrop MCP is committed to removing these barriers and enabling innovation without limits.
Raindrop MCP changes this equation completely. Instead of managing infrastructure, you describe your application to Claude Code, and the platform handles everything else—including database schemas and deployment URLs. Raindrop MCP also helps users plan and automate their development workflow, making it easier to schedule and manage each stage of the process.
Here’s what happened when we ran a hackathon with our team: 10 employees created 10 fully deployed applications in 2 hours. Not prototypes or demos—real applications with databases, APIs, and live URLs that users could actually access. The applications included a better way to socialize dogs, a family version of iCloud Photos, and an AI-powered support chat agent for auto mechanics, demonstrating how agents can perform autonomous support tasks.
One developer built a complete iOS app for rating coffee over a weekend, including backend APIs, user authentication, and a recommendation engine powered by vector search. Raindrop MCP is a powerful tool for developers looking to speed up their application development.
Traditional development workflow:
Raindrop MCP workflow:
When you connect Raindrop MCP to Claude Code, you gain access to a complete infrastructure orchestration system with MCP server implementations that can provision:
The system maintains context across conversations, so you can pause development, switch projects, or collaborate with team members while preserving all progress and architectural decisions. Users have control over their projects, account settings, and data privacy throughout the platform.
In today’s fast-paced world of AI, business, and technology, measuring and evaluating complexity is more than a technical challenge—it’s a strategic necessity. As organizations strive to innovate, understanding the intricate web of data sources, devices, and user interactions becomes essential for making informed decisions and staying ahead of the competition.
The Model Context Protocol (MCP) is a breakthrough technology designed to address this challenge. By providing a standardized way to connect AI models to a wide range of data sources—such as Google Drive, business applications, and even media platforms—MCP enables seamless access to the context needed for deep analysis. Whether you’re collecting data from multiple devices, tracking user activity across pages, or aggregating feedback from various channels, MCP ensures that your AI tools have the comprehensive context required to identify patterns and drive meaningful insights.
For businesses, this means the ability to connect and analyze everything from customer feedback to sales data, all while maintaining strict permissions and transparent access controls. Imagine a retail company using MCP to gather and analyze images, transaction records, and customer reviews from different sites and platforms. By representing this data in a unified context, the business can identify trends, reduce unnecessary fees, and enhance its services—gaining a real competitive advantage.
In research, evaluating complexity often involves studying dynamic systems, such as social networks or stock markets, where events and interactions are constantly changing. With tools like MCP, researchers can connect their AI models to live data streams, track changes in real time, and use advanced algorithms to understand how different factors influence outcomes. For example, analyzing stock market data might involve connecting to financial news, tracking market events, and using AI to identify patterns that could predict future changes.
Security and transparency are paramount when dealing with sensitive data, whether it’s personal images, financial records, or proprietary business information. MCP supports robust security protocols, ensuring that data collection and analysis are protected by encryption, secure authentication, and granular permissions. This commitment to data protection not only safeguards your property but also builds trust with users and stakeholders.
Ultimately, the ability to measure and evaluate complexity using advanced tools like the Model Context Protocol, AI, and machine learning is transforming how businesses and researchers operate. By connecting to diverse data sources and accessing the right context, organizations can better understand their subjects, represent complex systems accurately, and make smarter, data-driven decisions. In a world where change is constant and innovation is key, mastering complexity is the foundation for future success.
Unlike traditional AI development tools that work in isolation, Raindrop MCP is multiplayer by default. Every project creates a shared session that each member can join at any time, with every member able to contribute to the project. Product managers can define requirements, developers can implement features, and stakeholders can provide feedback—all within the same continuous workflow, including the ability to edit project requirements or features in real time.
We’ve architected Raindrop to serve AI-native startups, solopreneurs, and any small development team. Our customers include companies in manufacturing, financial services, media, and telecommunications—subject areas and sectors building warehouse automation systems and AI-powered trading bots.
As one of our beta users put it:
“Raindrop MCP eliminates the infrastructure learning curve entirely. Our PM can now deploy backend services just by describing what she wants to Claude Code. It’s like having a full DevOps team that never sleeps.”
Understanding the reasons behind infrastructure decisions or workflow changes has become much clearer for our users.
Setting up Raindrop MCP takes less than 5 minutes:
The documentation describes the setup process and features in detail. If you have any questions, want to request assistance, or need to contact our support team, please reach out—we’re here to help. You can also submit a question about specific new features or express your interest in advanced capabilities and future research directions.
By signing up, you agree to our beta terms and terms of service. Learn more about the platform’s features and benefits in our resources, including relevant information about connecting AI assistants. After the beta, users will have the option to buy premium features or services.
If you’re a technical PM with basic coding knowledge or a seasoned developer, Raindrop MCP adapts to your skill level. The more technical expertise you have, the more you can customize and optimize, but anyone can build and deploy production applications.
We’re currently in public beta as an open source project, and it’s completely free for early users. The response has been overwhelming and we are excited for the world to try it today.
Raindrop MCP represents a fundamental shift in how innovators approach application development. Instead of spending months learning infrastructure tools and managing deployment pipelines, developers can focus entirely on your application’s unique value proposition.
We’re making development faster and more accessible to anyone with good ideas and the drive to build them. In a world where AI applications are becoming the norm rather than the exception, having infrastructure that understands AI-native requirements is necessary.
Ready to transform your development workflow?
Sign up for public beta access at liquidmetal.run and join the AI-native developers already building with Claude Native infrastructure. For more information, visit our sign-up page.
The age of infrastructure complexity is over. The age of idea-to-deployment is here.
Raindrop MCP is currently available in public beta. Setup takes less than 5 minutes. Go to liquidmetal.run to get started or visit our sign-up page for more details.