Yesterday, Microsoft CEO Satya Nadella announced a major reorganization focused on AI platforms and tools, signaling the next phase of the AI revolution. Reading between the lines of Microsoft's announcement and comparing it to the emerging universal tools ecosystem, there are fascinating parallels that highlight why standardized, portable AI tools are critical for enterprise success.

The Platform Vision​

Nadella's memo emphasizes that we're witnessing an unprecedented transformation:

"Thirty years of change is being compressed into three years!"

He outlines a future where AI reshapes every layer of the application stack, requiring:

• New UI/UX patterns
• Runtimes to build and orchestrate agents
• Reimagined management and observability layers

The Universal Tools Answer​

This is where universal tools and platforms like mcp.run become critical enablers of this vision. Let's break down how:

1. Standardization & Scalability​

The new AI ecosystem demands standardized ways for systems to interact. Universal tools provide:

• Consistent interfaces for AI systems to access external services
• Dynamic capability updates without infrastructure changes
• Scalable architecture for adding new AI capabilities

2. Security & Enterprise Readiness​

As AI capabilities expand, security becomes paramount. Modern tool architectures offer:

• Built-in security controls and permissions management
• Sandboxed execution environments (like WebAssembly)
• Enterprise-grade safety across business processes

3. Cross-Platform & Universal Access​

Microsoft emphasizes AI reshaping every application layer. Universal tools support this by:

• Running anywhere WebAssembly is supported (browser, mobile, edge, server)
• Ensuring consistent behavior across platforms
• Enabling truly portable AI capabilities

4. Developer Experience & Tool Integration​

The future requires new ways of building and deploying AI capabilities:

• Unified management systems for AI tools
• Streamlined deployment and updates
• Rapid development and integration of new capabilities

5. Future-Proofing​

As Nadella notes, we're seeing unprecedented rate of change. Universal tools help by:

• Ensuring portability as AI expands to new environments
• Enabling discovery and updates without infrastructure rebuilds
• Creating standardized patterns for AI tool development

Bridging Vision and Reality​

Microsoft's reorganization announcement highlights the massive transformation happening in enterprise software. The success of this transformation will depend on having the right tools and platforms to implement these grand visions.

Universal tools provide the practical foundation needed to:

1. Safely adapt AI capabilities across different contexts
2. Maintain security and control
3. Enable rapid innovation and deployment
4. Support cross-platform compatibility

The Path Forward​

As we enter what Nadella calls "the next innings of this AI platform shift," the role of universal tools becomes increasingly critical. They provide the standardized, secure, and portable layer needed to implement ambitious AI platform visions across different environments and use cases.

For enterprises looking to succeed in this AI transformation, investing in universal tools and standardized approaches isn't just good practice—it's becoming essential for success.

We're working with companies and angencies looking to enrich AI applications with tools -- if you're considering how agents play a role in your infrastructure or business operations, don't hesitate to reach out!

This post analyzes the intersection of Microsoft's January 14, 2025 AI platform announcement with emerging universal tools patterns. For more information, see Microsoft's blog post and mcp.run's overview of universal tools.

Compile once, run anywhere? You bet! After our mcp.run OpenAI integration and some teasing, we're excited to launch mcpx4j, our client library for the JVM ecosystem.

Built on the new Extism Chicory SDK, mcpx4j is a lightweight library that leverages the pure-Java Chicory Wasm runtime. Its simple design allows for seamless integration with diverse AI frameworks across the mature JVM ecosystem.

To demonstrate this flexibility, we've prepared examples using popular frameworks:

• Spring AI brings extensive model support; our examples focus on OpenAI and Ollama modules, but the framework makes it easy to plug in a model of your choice. Get started with our complete tutorial.

• LangChain4j offers a wide range of model integrations. We showcase implementations with OpenAI and Ollama, but you can easily adapt them to work with your preferred model. Check out our step-by-step guide to learn more.

One More Thing. mcpx4j doesn't just cross framework boundaries - it crosses platforms too! Following our earlier Android experiments, we're now sharing our Android example with Gemini integration, along with a complete step-by-step tutorial.

We can't wait to see what you'll create with mcpx4j. Try it out and share your feedback with us!

Although an open standard, MCP has primarily been in the domain of Anthropic products. But what about OpenAI? Do we need to wait for them to add support? How can we connect our tools to o3 when it releases this month?

Thanks to the simplicity and portability of mcp.run servlets, you don't need to wait. Today we're announcing the availability of our initial OpenAI support for mcp.run.

We're starting with support for OpenAI's node library, but we have more coming right around the corner.

To get started, see the mcpx-openai-node repo or see our tutorial.

We hope you're having a great time with friends and family during these holidays!

On December 25th, an exciting gift arrived: the Chicory pure-Java Wasm runtime released its final 1.0 version! Along with it, the Extism Chicory SDK received significant updates, including an experimental HTTP client. This timing couldn't be better for experimenting with mcp.run!

As previously discussed, WebAssembly is the foundation of this technology. Every servlet you install on the mcpx server is powered by a Wasm binary: mcpx fetches these binaries and executes commands at the request of your preferred MCP Client.

WebAssembly's Portability Superpower​

This Wasm core is what enables mcpx to run on all major platforms from day one. However, while mcpx is currently the primary consumer of the mcp.run service, it's designed to be part of a much broader ecosystem.

In fact, while holiday celebrations were in full swing, we've been busy developing something exciting!

Recently, we demonstrated how to integrate mcp.run's Wasm tools into a Java host application. In the following examples, you can see mcp.run tools in action, using the Google Maps API for directions:

• You can now fetch any mcp.run tool with its configuration and connect it to models supported by Spring AI (See demos on 𝕏 and 🦋)

• Similarly, you can connect any mcp.run tool to models supported by LangChain4j, including Jlama integration (See demos on 𝕏 and 🦋)

This goes beyond just connecting to a local mcpx instance (which works seamlessly). Thanks to Chicory, we're running the Wasm binaries directly within our applications!

With this capability to run MCP servlet tools via mcp.run locally in our Java applications, we tackled an exciting challenge...

Android Integration: A New Frontier​

We discovered that a pure-Java Wasm runtime ports remarkably well to Android. Our initial proof-of-concept shows promising results:

What you're seeing is an Android application that:

• Fetches servlets and configurations from mcp.run
• Integrates with Google's Gemini AI
• Interfaces with the Google Maps servlet
• All running directly on the device!

The Importance of On-Device AI Tools​

While external service calls are often necessary (like our demo's use of the Google Maps API), AI is becoming increasingly personal and embedded in our daily lives. As AI and agents migrate to our personal devices, the traditional model of routing everything through internet services becomes less ideal. Consider these scenarios:

• Your banking app shouldn't need to send statements to a remote finance agent
• Your health app shouldn't transmit personal records to external telehealth agents
• Personal data should remain personal

As local AI capabilities expand, we'll see more AI systems operating entirely on-device, and their supporting tools must follow suit.

Join us​

While this implementation is still in its early stages, it already demonstrates impressive capabilities. The Wasm binary servlet runs seamlessly on-device, is fully sandboxed (only granted access to Google Maps API), and executes quickly.

We're working to refine the experience and will share more developments soon. We're excited to see what you will create with these tools! If you're interested in exploring these early demos, please reach out!

"The notion that business applications exist, that's probably where they'll all collapse in the agent era."​

If you haven't seen this interview yet, it's well worth watching. Bill and Brad have a knack for bringing out insightful perspectives from their guests, and this episode is no exception.

How does SaaS collapse?​

Satya refers to how most SaaS products are fundamentally composed of two elements: "business logic" and "data storage". To vastly oversimplify, most SaaS architectures look like this:

Satya proposes that the upcoming wave of agents will not only eliminate the UI (designed for human use, click-ops style), but will also move the "CRUD" (Create - Read - Update - Delete) logic entirely to the LLM layer. This shifts the paradigm to agents communicating directly with databases or data services.

As someone who has built many systems that could be reduced to "SaaS", I believe we still have significant runway where the CRUD layer remains separate from the LLM layer. For instance, getting LLMs to reliably handle user authentication or consistently execute precise domain-specific workflows requires substantial context and specialization. While this logic will persist, certain layers will inevitably collapse.

The Collapse of the UI (as we know it)​

Satya's key insight is that many SaaS applications won't require human users for the majority of operations. This raises a crucial question: in a system without human users, what form does the user interface take?

This transformation represents a fundamental shift, where the Model Context Protocol (MCP) will play the biggest role in software since Docker.

The agent becomes the UI.​

However, this transition isn't automatic. We need a translation and management layer between the API/DB and the agent using the software. While REST APIs and GraphQL exist for agents to use, MCP addresses how these APIs are used. It also manages how local code and libraries are accessed (e.g., math calculations, data validation, regular expressions, and any code not called over the network).

MCP defines how intelligent machines interact with the CRUD layer. This approach preserves deterministic code execution rather than relying on probabilistic generative outputs for business logic.

Covering Your SaaS​

If you're running a SaaS company and haven't considered how agent-based usage will disrupt the next 1-3 years, here's where to start:

• Reimagine your product as purely an API. What does this look like? Can every operation be performed programmatically?

• Optimize for machine comprehension and access. MCP translates your SaaS app operations into standardized, machine-readable instructions. Publishing a Servlet offers the most straightforward path to achieve this.

• Plan for exponential usage increases. Machines will interact with your product orders of magnitude faster than human users. How will your infrastructure handle this scale?

While the exact timeline remains uncertain, these preparations will position you for the inevitable shift in how SaaS (and software generally) is consumed in an agent-driven world. The challenge of scaling and designing effective machine-to-machine interfaces is exciting and will force us to think differently.

The Next Mode of Software Delivery​

There's significant advantage in preparing early for agent-based consumers. Just as presence in the Apple App Store during the late 2000s provided an adoption boost, we're approaching another such opportunity.

In a world where we're not delivering human-operated UIs, and APIs aren't solely for programmer integration, what are we delivering?

MCP-based Delivery​

If today's UI is designed for humans, and tomorrow's UI becomes the agent, the architecture evolves to this:

MCP provides the essential translation layer for cross-system software integration. The protocol standardizes how AI-enabled applications or agents interact with any software system.

Your SaaS remains viable as long as it can interface with an MCP Client.​

Implementation requires developing your application as an MCP Server. While several approaches exist, developing and publishing an MCP Servlet offers the most efficient, secure, and portable solution.

As an MCP Server, you can respond to client queries that guide the use of your software. For instance, agents utilize "function calling" or "tool use" to interact with external code or APIs. MCP defines the client-server messages that list available tools. This tool list enables clients to make specific calls with well-defined input parameters derived from context or user prompts.

A Tool follows this structure:

{
  name: string;          // Unique identifier for the tool
  description?: string;  // Human-readable description
  inputSchema: {         // JSON Schema for the tool's parameters
    type: "object",
    properties: { ... }  // Tool-specific parameters
  }
}

For example, a Tool enabling agents to create GitHub issues might look like this:

{
  "name": "github_create_issue",
  "description": "Create a GitHub issue",
  "inputSchema": {
    "type": "object",
    "properties": {
      "title": { "type": "string" },
      "body": { "type": "string" },
      "labels": { "type": "array", "items": { "type": "string" } }
    }
  }
}

With this specification, AI-enabled applications or agents can programmatically construct tool-calling messages with the required title, body, and labels for the github_create_issue tool, submitting requests to the MCP Server-implemented GitHub interface.

Prepare Now​

Hundreds of applications and systems are already implementing MCP delivery, showing promising adoption. While we have far to go, Satya isn't describing a distant future—this transformation is happening now.

Just as we "dockerized" applications for cloud migration, implementing MCP will preserve SaaS through the App-ocalypse.

The sooner, the better.

If you're interested in MCP and want to learn more about bringing your software to agent-based usage, please reach out. Alternatively, start now by implementing access to your SaaS/library/executable through publishing to mcp.run.

A few weeks ago, Anthropic announced the Model Context Protocol (MCP). They describe it as:

[...] a new standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments.

While this is an accurate depiction of its utility, I feel that it significantly undersells what there is yet to come from MCP and its implementers.

In my view, what Docker (containers) did to the world of cloud computing, MCP will do to the world of AI-enabled systems.

Both Docker and MCP provide machines with a standard way to encapsulate code, and instructions about how to run it. The point where these clearly diverge, (aside from being packaging technology vs. a protocol) is that AI applications are already finding their way into many more environments than where containers are optimal software packages.

AI deployment diversity has already surpassed that of the cloud. MCP gives us a way to deliver and integrate our software with AI systems everywhere!

AI applications, agents, and everything in-between need deterministic execution in order to achieve enriched capabilities beyond probabilistic outputs from today's models. A programmer can empower a model with deterministic execution by creating tools and supplying them to the model.

So, part of what we're announcing today is the concept of a portable Wasm servlet, an executable code artifact that is dynamically & securely installed into an MCP Server. These Wasm servlets intercept MCP Server calls and enable you to pack tons of new tools into a single MCP Server.

More on this below, but briefly: Wasm servlets are loaded into our extensible MCP Server: mcpx, and are managed by the corresponding registry and control plane: mcp.run.

If any of the predictions prove true about the impact of AI on software, then it is reasonable to expect a multitude of software to implement at least one side of this new protocol.

MCP Adoption​

Since the announcement, developers around the world have created and implemented MCP servers and clients at an astonishing pace; from simple calculators and web scrapers, to full integrations for platforms like Cloudflare and Browserbase, and to data sources like Obsidian and Notion.

Anthropic certainly had its flagship product, Claude Desktop, in mind as an MCP Client implementation, a beneficiary to access these new capabilities connecting it to the outside world. But, to go beyond their own interest, opening up the protocol has paved the way for many other MCP Client implementations to also leverage all the same Server implementations and share these incredible new capabilities.

So, whether you use Claude Desktop, Sourcegraph Cody, Continue.dev, or any other AI application implementing MCP, you can install an MCP Server and start working with these MCP tools from the comfort of a chat, IDE, etc.

Want to manage your Cloudflare Workers and Databases?

→ Install the Cloudflare MCP Server.

Want to automate browsing the web from Claude?

→ Install the Browserbase MCP Server.

Want to use your latest notes from your meetings and summarize a follow-up email?

→ Install the Obsidian MCP Server.

Exciting as this is, there is a bit of a low ceiling to hit when every new tool is an additional full-fledged MCP Server to download and spawn.

The Problem​

Since every one of these MCP Servers is a standalone executable with complete system access on your precious local machine, security and resource management alarms should be sounding very loudly.

As the sprawl continues, every bit of code, every library, app, database and API will have an MCP Server implementation. Executables may work when n is small, you can keep track of what you've installed, you can update them, you can observe them, you can review their code. But when n grows to something big, 10, 50, 100, 1000; what happens then?

The appetite for these tools is only going to increase, and at some point soon, things are going to get messy.

The Solution​

Today we're excited to share two new pieces of this MCP puzzle: mcpx (the extensible, dynamically updatable MCP Server) and mcp.run (a corresponding control plane and registry) for MCP-compatible "servlets" (executable code that can be loaded into mcpx). Together, these provide a secure, portable means of tool use, leveraging MCP to remain open and broadly accessible. If you build your MCP Server as a "servlet" on mcp.run, it will be usable in the most contexts possible.

How?​

All mcpx servlets are actually WebAssembly modules under the hood. This means that they can run on any platform, on any operating system, processor, web browser, or device. How long will it be until the first MCP Client application is running on a mobile phone? At that point your native MCP Server implementation becomes far less useful.

MCP Servers over HTTP / SSE​

Can we call these tools via HTTP APIs? Yes, the protocol already specifies a transport to call MCP Servers over a network. But it's not implemented in Claude Desktop or any MCP Client I've come across. For now, you will likely be using the local transport, where both the MCP Client and Server are on your own device.

Portability​

One major differentiator and benefit to choosing mcp.run and targeting your MCP servers to mcpx servlets is portability. AI applications are going to live in every corner of the world, in all the systems we interact with today. The tools these apps make calls to must be able to run wherever they are needed - in many cases, fully local to the model or other core AI application.

If you're working on an MCP Server, ask yourself if your current implementation can easily run inside a database? In a browser? In a web app? In a Cloudflare Worker? On an IoT device? On a mobile phone? mcp.run servlets can!

We're not far from seeing models and AI applications run in all those places too.

By publishing MCP servlets, you are future-proofing your work and ensuring that wherever AI goes, your tools can too.

mcpx​

To solve the sprawling MCP Server problem, mcpx is instead a dynamic, re-programmable server. You install it once, and then via mcp.run you can install new tools without ever touching the client configuration again.

We're calling the tools/prompts/resources (as defined by the protocol), "servlets" which are managed and executed by mcpx. Any servlet installed to mcpx is immediately available to use by any MCP Client, and can even be discovered dynamically at runtime by a MCP Client.

You can think of mcpx kind of like npm or pip, and mcp.run as the registry and control plane to manage your servlets.

mcp.run​

We all like to share, right? To share servlets, we need a place to keep them. mcp.run is a publishing destination for your MCP servlets. Today, your servlets are public, but soon we will have the ability to selectively assign access or keep them private at your discretion.

As mentioned above, currently servlets are installed locally and executed by a client on your machine. In the future, we plan on enabling servlets to run in more environments, such as expanding mcp.run to act as a serverless environment to remotely execute your tools and return the results over HTTP.

You may even be able to call them yourself, outside the context of an MCP Client as a webhook or general HTTP endpoint!

Early Access​

Last week, mcpx and mcp.run won Anthropic's MCP Hackathon in San Francisco! This signaled to our team that we should add the remaining polish and stability to take these components into production and share them with you.

Today, we're inviting everyone to join us. So, please head to the Quickstart page for instructions on how to install mcpx and start installing and publishing servlets!

Here's a quick list of things you should try out:

• have Claude Desktop log you in to mcp.run
• get Claude to search for a tool in the registry (it will realize it needs new tools on its own!)
• install and configure a tool on mcp.run, then call it from Claude (no new MCP Server needed)
• publish a servlet, compiling your library or app to WebAssembly (reach out if you need help!)

As things are still very early, we expect you to hit rough edges here and there. Things are pretty streamlined, but please reach out if you run into anything too weird. Your feedback (good and bad) is welcomed and appreciated.

We're very excited about how MCP is going to impact software integration, and want to make it as widely adopted and supported as possible -- if you're interested in implementing MCP and need help, please reach out.