The Model Context Protocol (MCP) is transforming how AI agents access tools and data. But building MCP servers? That’s been a different story entirely.
Until now, creating an MCP server meant diving deep into TypeScript, wrestling with JSON-RPC protocols, and handling all the infrastructure complexity yourself. There are a few solutions out there, but they’re notoriously painful to use. Most developers face days of development time just to get a basic server running.
Raindrop just changed that equation completely.
mcp_service "storage-mcp" {
visibility = "public"
}
That’s it. Add this single stanza to your Raindrop manifest, and your app becomes a production-ready MCP server with:
The MCP server acts as a function that relates inputs (such as data sources) to outputs (AI-powered services), much like a production function in economics that connects resources to results.
This breakthrough is possible thanks to the underlying theory that enables seamless integration and deployment, allowing you to focus on building value rather than managing complexity.
Consider building an MCP storage server. The traditional approach requires:
Automating these processes can significantly reduce the number of manual jobs required, sometimes resulting in jobless growth where productivity increases without a corresponding rise in employment opportunities.
Time investment: Days to weeks
The Raindrop approach:
Raindrop streamlines the entire process of deploying MCP servers, simplifying each step from software development to deployment. The proportion of time saved is substantial—Raindrop reduces deployment time from days or weeks to just minutes, representing a dramatic improvement in efficiency.
Time investment: 14 minutes
Here’s where it gets really interesting. The token efficiency of this approach unlocks something unprecedented: AI agents can now create their own MCP servers on-demand.
Instead of an agent burning hundreds of tokens trying to write MCP protocol code, it can simply say:
Create an object storage bucket attachable via an MCP server and give me back the endpoint
Raindrop understands this plain English request, spins up the infrastructure, and returns a working MCP endpoint. The agent gets unlimited storage capability without burning tokens on implementation details. Additionally, the agent can produce new storage resources or endpoints on demand, enabling flexible and scalable workflows that can be integrated across the entire organization, supporting better management of internal systems and organizational structures.
This isn’t just about efficiency—it’s about emergent AI workflows. Agents can now:
Raindrop’s visibility settings give you precise control over your MCP server’s accessibility:
Behind every seamless MCP server deployment lies a robust server infrastructure, purpose-built to support the Model Context Protocol and the next generation of AI models. MCP servers, crafted with official SDKs, form the backbone of this protocol—connecting AI applications to a wide array of data sources and tools with just a single line of configuration. The very first MCP server showcased the protocol’s flexibility, and since then, organizations across industries have leveraged MCP servers to power everything from large language models to specialized production workflows.
By enabling secure, scalable access to context-rich data, MCP servers allow developers to focus on creating features and optimizing processes, rather than wrestling with infrastructure. This is especially critical in production environments, where the quality and performance of server infrastructure directly impact the efficiency and cost-effectiveness of the manufacturing industry, manufacturing, finance, healthcare, and more. For instance, integrating Google Drive as a data source through an MCP server gives teams controlled, auditable access to essential information—streamlining the creation of new tools and models without compromising on security or compliance. Increased efficiency and output from these integrations can also enhance an organization’s ability to pay taxes, salaries, and profits, reflecting the broader economic benefits of improved production workflows.
The ability to connect AI models to diverse data sources via MCP servers is a game-changer for organizations aiming to enhance their production processes. It empowers developers to build, scale, and iterate rapidly, ensuring that the focus remains on delivering value and innovation. Ultimately, the quality of your server infrastructure is a decisive factor in the success of your AI-powered solutions, making MCP servers and the Model Context Protocol indispensable tools for modern business.
As AI applications and MCP servers become integral to business operations, security has never been more important. Every deployment of an AI model or MCP client introduces new data flows and potential vulnerabilities, making it essential for organizations to implement robust security strategies from the outset. This means leveraging official SDKs, enforcing strict access controls, encrypting sensitive data, and continuously monitoring systems for unusual activity.
MCP servers are designed with security at their core, ensuring that data is transmitted and processed safely throughout the production lifecycle. By using mathematical expressions and statistical analysis, organizations can identify patterns and anomalies in real time—enabling them to respond swiftly to potential threats and maintain the integrity of their systems. In industries where production processes rely on the seamless exchange of data, a single breach can disrupt operations, incur significant costs, and damage reputation.
To mitigate these risks, organizations must focus on developing and implementing security protocols that are tailored to their specific needs. This includes regular analysis of system performance, proactive identification of vulnerabilities, and the use of tools that support secure deployment and management of AI models. By prioritizing security at every stage—from development to deployment—businesses can protect their data, ensure compliance, and maintain the trust of their customers and partners.
In the rapidly evolving world of AI and MCP, a secure server infrastructure isn’t just a best practice—it’s a fundamental requirement for success.
Unlike development-focused MCP implementations, Raindrop’s mcp_service feature delivers enterprise-grade capabilities:
Building and deploying AI models at scale requires more than just powerful algorithms—it demands robust frameworks and tools that simplify the entire process. Official SDKs are essential in this landscape, abstracting the complexities of the Model Context Protocol so developers can focus on creating impactful features rather than wrestling with low-level implementation details. MCP servers, such as those designed for Google Drive, Slack, and GitHub, showcase the flexibility of the protocol, allowing secure and efficient access to a wide range of data sources.
These tools enable developers to construct sophisticated workflows and agents on top of large language models, making it easier to analyze and interpret data using mathematical expressions and advanced algorithms. The result is a streamlined development process where new AI-powered features and services can be created, deployed, and managed with minimal friction. By leveraging the power of MCP and official SDKs, organizations can accelerate the creation of innovative AI applications, ensuring that their models are always connected to the right context and data.
We’re not building a marketplace—we’re providing the easiest way to create production MCP servers, period. The real ecosystem will emerge from what developers and agents build with this capability. Companies across various industries, such as the manufacturing industry and the nuclear power industry, can leverage MCP servers for specialized needs.
Imagine specialized MCP servers for:
A new integration platform is set to be launched by fall 2026, further expanding the ecosystem and providing more options for future development.
The Model Context Protocol thrives within a dynamic and collaborative AI community. As an open-source project, MCP invites developers, enterprises, and early adopters to contribute to the evolution of context-aware AI. Comprehensive documentation, best practices, and implementation guides are readily available, making it easy for anyone to get started with building or deploying MCP servers.
Platforms like GitHub serve as hubs for MCP-related projects, where community-built servers, frameworks, and tools are shared and improved upon. This open exchange of ideas and strategies not only accelerates the development of new features but also helps identify emerging trends and technologies. By participating in the MCP ecosystem, developers gain access to a wealth of resources and the collective expertise of a global network, ensuring that their AI models and services remain at the forefront of innovation.
Contributing to AI development is a multifaceted process that extends far beyond building models—it encompasses the creation, deployment, and continuous improvement of the entire AI ecosystem. Developers play a crucial role by building MCP servers, which act as bridges connecting AI models to a variety of data sources and tools. These servers enable seamless data exchange and integration, making it easier to develop and deploy AI applications across different platforms and industries.
Another key area of contribution is the development of official SDKs, which simplify the process of implementing the Model Context Protocol and accelerate the creation of new AI services. By providing standardized tools and frameworks, SDKs empower developers to focus on innovation rather than infrastructure, streamlining the deployment of AI models into production environments.
Organizations and individuals also contribute by creating and curating datasets, developing new algorithms, and rigorously testing and evaluating models to ensure high-quality outputs. These efforts are essential for advancing the field, as they drive the creation of more robust and efficient AI models that can be deployed in real-world scenarios. For example, AI applications such as chatbots, virtual assistants, and automated analysis tools are now widely used to improve production processes, reduce operational costs, and enhance customer service across various sectors.
By actively participating in the development and deployment of AI models, tools, and MCP servers, contributors help shape the future of artificial intelligence. Their work not only advances technology but also delivers tangible benefits to organizations—enabling smarter decision-making, more efficient processes, and greater value for customers and stakeholders. As the AI landscape continues to evolve, the collective efforts of developers, organizations, and the broader community will remain a driving force behind ongoing innovation and progress.
Looking ahead, the future of AI is set to be defined by ever-closer integration between models, data sources, and the tools that connect them. Protocols like MCP will play a pivotal role in enabling AI systems to access, interpret, and act on information from a diverse array of sources, resulting in smarter, more adaptive applications. As AI models become more sophisticated, the quality and relevance of their outputs will continue to improve, driving new levels of efficiency and value in production, management, and service delivery.
Open-source development and active community involvement will ensure that advancements in AI remain transparent, ethical, and responsive to real-world needs. As the economy and job market evolve, AI will increasingly influence how organizations manage data, optimize processes, and deliver services. Ongoing innovation in MCP and related technologies will be essential for organizations seeking to stay competitive, maximize the quality of their outputs, and harness the full potential of AI in a rapidly changing world.
Ready to turn your Raindrop app into an MCP server?
For pricing and scaling details, visit liquidmetal.ai/pricing.
Building MCP servers just became as simple as adding a configuration line, thanks to technology that streamlines server deployment. Whether you’re a developer who needs production-ready tooling or an AI agent that needs specialized capabilities on-demand, Raindrop’s mcp_service feature removes every barrier between idea and implementation.
The age of disposable, purpose-built MCP servers has arrived. MCP servers can now be produced rapidly and at scale, transforming how digital infrastructure is delivered. What will you build?
Want to see this in action? Try building your first MCP-enabled Raindrop app and experience the magic of one-stanza deployment. MCP servers are quickly becoming a commodity in the AI development landscape, making advanced infrastructure more accessible than ever.