AI developers face a persistent challenge: integrating multiple frameworks, redefining the same tools across different platforms, and managing complex custom code just to make their applications work. This fragmentation slows development and creates unnecessary complexity.
The Model Context Protocol (MCP) addresses this problem head-on. Developed by Anthropic and now adopted by major players including OpenAI and Microsoft, MCP provides a standardized way for AI applications to communicate with tools and resources. Think of it as USB-C for AI: one protocol that works everywhere.
This article breaks down what MCP is, how it works, why it's gaining rapid adoption across the industry, and how to start implementing it with high-performing open-source models.
What is MCP?
MCP stands for Model Context Protocol. It emerged from Anthropic, a leading AI lab, as a basic standard for AI applications to universally talk to each other. This standard gained significant traction after its introduction early last year.
The best way to think about MCP is like a USB-C connector for AI apps. Just as USB-C standardized charging stations and made things significantly easier for electronics in general, MCP aims to achieve the same standardization for AI applications.
With MCP, an agent can talk to an MCP server, and an MCP host such as Claude can connect to all sorts of things. In essence, it's a protocol being used to manage AI services and tools.
Technical Architecture of the Model Context Protocol
Breaking down MCP technically, there are three main components:
- Servers: These host the functionality. a GitHub server.
- Clients: These receive information and are able to consume it.
- Hosts: These facilitate the interaction between servers and clients, with examples including Cursor, Windsurf, and Claude.
Two Most Common Use Cases of MCP
#1. Code Development
Code development became an instant use case due to the nature of the medium. Some of the first hosts were applications like Cursor, Windsurf, and Claude. This created a powerful workflow enhancement: developers working in Cursor could suddenly access an MCP server containing documentation for niche products they're working on.
The workflow now includes not only web search information but also a new set of tools brought in by MCP servers, making this the killer use case that has driven adoption to where it stands today.
#2. Expanded Capabilities: Resources and Prompts
While tool integration is the most common use case, MCP actually goes much beyond that.
According to the deep learning course that Anthropic recently released on MCP, two additional key features stand out:
- Resources: The ability to bring in documentation and other resources in a uniform way. Companies can bring in specific information like hardware specifications as resources, making them easily consumable en masse.
- Prompts: With the ongoing movement in prompt engineering, MCP allows for template management to get the necessary performance out of models.
The Power of Standardization that MCP Brings
The key principle behind MCP and similar standards is reproducibility. Anyone building AI agents currently faces a common problem: having to use multiple frameworks and define tools repeatedly.
MCP follows the old data acronym: write once, read many. You write and define something once, then many clients can read and consume it, making the entire architecture highly useful.
Whether using MCP within agents, desktop clients, or basic applications, it's incredibly powerful to be able to use and consume these tools in various ways. The shift toward general-purpose agents means they can call multiple specialized models, and having a uniform set of tools to pick from for any task becomes invaluable.
MCP: A New Design Pattern for AI Applications Emerges
This standardization fundamentally changes how applications are built. The agent comes in and asks, "What tools do I have available to me today?" and that entire interface becomes straightforward.
It's becoming a requirement for AI builders to have MCP integrated into what they're doing because users don't want to get into custom code. They simply want to use a uniform JSON format that will fetch a file or executable and handle it automatically.
With this standard in place, app builders no longer need to think about what tools they need but rather how to enable MCP to run their application. This is a different design pattern that's enabling things to move much faster because general-purpose agents don't necessarily have to define their tools upfront, but they can be given the tools they need and will figure out what to do with them.
MCP Industry Adoption and Momentum
This is one of the big reasons why the ecosystem is adopting MCP. The uptick really started to happen when it began as Anthropic's protocol. After a couple of months, there was significant adoption. Then OpenAI came in, and everyone realized this might actually become a standard.
At the time of this conversation (August 2025, right after Microsoft Build), Windows integration of MCP was announced for desktop, agents, Foundry, and Azure. All of these major players are adopting this protocol, driving momentum to establish it as a standard. This is what the industry needs right now: more standardization of these tools.
The big labs are also adopting MCP because it solves a huge pain point for them. No one writes more AI code than they do, and standardization is music to their ears. It allows their products to be more usable. Models become more usable when they can plug into this standard.
The almost ubiquitous integration shows just how much universality MCP has. These tools by definition are JSON files: very simple things that can be abstracted. There are uniform methods for discovering what tools are available and using them.
Addressing Enterprise Concerns for Using MCP
One area where MCP took a little longer was addressing the enterprise question. When a new standard emerges, many people ask, "This is great for developers, but what about the enterprise? How can the enterprise CIO or CTO adopt this technology?"
These big labs addressed the elephant in the room: security. It's just code from the internet. Who authored that code? It's similar to an app from the app store; there's safety infrastructure for a reason. Users don't just download packages indiscriminately.
With OpenAI and Microsoft coming in, some of these elements are placed behind security walls. This will drive even more usage because once enterprises determine it's acceptable to use, adoption will increase significantly. Great open-source solutions are emerging as well.
Open Source Performance with MCP
Open source is a key component of the MCP ecosystem. There's always debate about whether to use only closed source, whether to switch to open source, and whether open-source models will work well in these use cases.
The Latest Models Running on SambaNova
At SambaNova, we’re huge proponents of open source, because we think it will win.
What we’re seeing with this latest crop of models, the Mavericks and Qwens of the world, is that they are more advanced in the way they can follow instructions and work with structured output and tool calling. Now that they can consume these tools, very complex agentic applications are being built.
Previously, only certain closed-source models could handle these tasks, but now there's really good performance from models like Maverick on SambaCloud. It works exceptionally well with context length and general usage.
Models, like DeepSeek V3, are very performant; they are excellent at planning, following instructions, and calling MCP tools as needed.
According to Artificial Analysis, for non-reasoning use cases, open-source models are actually leading compared to closed-source models.
DeepSeek V3 is one of the gold standards right now in terms of cost effectiveness, speed when run on platforms like SambaNova, and accuracy. On all three factors, there's great utilization happening.
Why Speed in AI Applications Matters
Speed really matters in these applications. MCP shows that when builders are given more tools, they will use those tools. More tools lead to more LLM calls and expanded capabilities. This chain of events makes inference speed even more important.
Previously, there might be three LLM calls because there was a custom tool in a Python file. Now there's an entire MCP server that could have 50 tools available. With better-performing open-source models able to use all of these tools, the volume increases significantly.
This is a significant component of why there's an uptick in adoption. The speed matters tremendously. When running models like DeepSeek with world record performance and chaining together 20-30 calls in applications plus tool use, it really starts to add up.
Many people see demos that might seem sluggish, but once the performance of inference and tool calling increases, because it's all being packaged to talk to external databases, the difference is substantial.
The Autonomous Agent Breakthrough
What's most impressive about open source at the moment is the "aha moment" happening. People are realizing these systems can actually work pretty well autonomously. They can be given a task, figure it out, and get the right tools.
What people will be able to achieve within the next two to three months — timeframes that used to be six to nine months — keep getting shorter. The speed of innovation in this industry is remarkable, with something new emerging every day.
Getting Started with MCP
Platforms and Model Recommendations
For those trying out models, SambaCloud offers free access to fast, high-performing models that are well-suited for MCP integration. DeepSeek and Maverick are leading-edge resources that people can get started with quickly and easily.
Smaller models shouldn't be forgotten either. Llama 3 8B and Qwen3 are excellent performers. With open source, the right set of models can be assembled for specific tasks. With standards like MCP making them easier to run, really good performance can be achieved.
For those paying for closed-source providers, swapping in open-source alternatives is cheaper, and with the unification around these standards, real-world results are being achieved.
Complementary Standards: Agent2Agent (A2A)
Many people ask about MCP and then wonder about agents. It's important to touch on some of the complementary standards that are emerging.
There's significant debate in the industry about Agent2Agent (A2A), which is Google's standard. This standard is primarily built for unifying the way agents talk to each other. MCP is unifying the way tools and resources talk to each other. People are starting to build with A2A in some of the big frameworks.
For those who want to get ahead of the curve, it's worth looking at A2A because these standards are complementary: they do not conflict with each other as supported by both Google and Anthropic.
When thinking about the architecture, agents are defined in many places, but tools are defined once. The next logical question is: why would agents be defined in many places? A2A addresses that question, and developments in this space represent exciting new frontiers in AI standardization.
We’re doing some A2A work on SambaNova, so you can watch out for that.
Conclusion
MCP is transforming AI development by standardizing how applications communicate with tools and resources. With adoption from major players like Anthropic, OpenAI, and Microsoft — combined with strong performance from open-source models — the protocol is becoming essential infrastructure for modern AI applications.
The "write once, read many" approach eliminates redundant work, while high-performing models on platforms like SambaNova make implementation practical and cost effective. As innovation cycles compress from months to weeks, integrating MCP now positions developers at the forefront of this shift.
Ready to get started? Visit SambaCloud for free access to high-performing models optimized for MCP integration.