AI agents are becoming more capable than ever, not just in generating text or images, but in taking actions—navigating websites, automating workflows, querying APIS, and testing applications. But behind this growing intelligence is a need for structured, reliable communication between large language models (LLMS) and the tools they use. That’s where AI MCP Servers come in.
MCP, or Model Context Protocol, represents a standardised approach for letting AI models talk to external tools. Whether filling out forms, running tests, or navigating complex systems, an MCP server acts as the “brainstem” between an LLM’s reasoning and real-world execution.
By the end of this post, you will have a good understanding of what a MCP server is.
What is an AI MCP Server?
An AI MCP Server is a system that provides an interface between an AI model and a collection of tools it can use. It follows a structured Model Context Protocol to interpret instructions and trigger actions through tool APIS. Model Context Protocol has been dubbed the USB of AI. It works by sending intent-based requests—the MCP server handles execution, validation, and returns results in a structured response.
👉 Want to see how this works in action? Check out the MCP Server open-source implementation by BrowserStack on GitHub, and watch this short explainer video to understand how it connects AI agents to real-world tools seamlessly:
Benefits of Using an MCP Server
An MCP server makes it much easier for AI systems to use real-world tools in a reliable and safe way. Think of it as the translator and controller between the AI's brain and the outside world. Here are some of the key benefits it brings:
- It makes things more consistent.
Every tool—whether it's a browser, a form filler, or a file uploader—follows the same basic structure when it talks to the AI. That means you don’t have to build new systems every time you add a new tool. The AI always knows how to “ask” for an action, and the MCP server knows how to carry it out.
- It’s easy to expand.
If you want to add more tools or actions, you don’t need to redesign everything. MCP servers are built in a modular way, so you can plug in new tools like building blocks. This is great for growing systems or trying out new ideas quickly.
- It remembers what’s going on.
Many tasks, like logging in or filling out multi-step forms, require memory. An MCP server can manage session data—things like login states or previous inputs—so the AI doesn’t have to start over every time. This lets agents complete longer, more realistic workflows.
- It’s more reliable.
Without an MCP server, the AI might send incorrect or unsafe commands to tools. But with an MCP in place, every action is validated and checked first. If something looks wrong, like a missing field or invalid URL, the server can catch it before it becomes a problem.
- It helps with debugging.
When something goes wrong (and it will), the MCP server can provide helpful info like logs, screenshots, or detailed error messages. This makes it much easier to understand what happened and fix it, whether you're a developer or an AI engineer.
In short, a MCP server helps AI do things better, safer, and with more control. It creates a solid foundation for building agents that act with confidence.
Challenges and Considerations
While AI MCP servers are powerful, building and using them does come with some challenges. These are important to understand before diving in.
- Different tools behave in different ways.
Not all tools are built the same. A browser automation tool like NightwatchJS works very differently from a database or a file uploader. Wrapping all of these into a common format can be tricky. You need to design the server carefully so that it can understand each tool’s unique needs while still speaking one common language with the AI.
- Security matters a lot.
MCP servers often perform actions on behalf of the AI, like logging into websites or uploading data. It could open the door to abuse or data leaks. You’ll need to ensure the server checks inputs, limits risky behaviour, and protects sensitive information.
- Tools don’t always work perfectly.
Sometimes tools fail—maybe a website is down, or a form has changed. The MCP server has to handle these situations gracefully. Instead of crashing or confusing the AI, it should return a clear error message explaining what went wrong. This helps the AI decide what to do next or when to try again.
- Speed and performance can become an issue.
As your AI agents get smarter and more active, the number of tool requests can grow quickly. This can slow things down, especially if sessions or states have to be remembered between steps. You’ll want to think about ways to optimise, like using caching or background workers to handle heavy tasks.
- Scaling isn’t just technical.
If you plan to support many users or run multiple AI agents at once, your MCP server must be designed to scale. That means thinking ahead about how sessions are managed, how tasks are queued, and how to avoid bottlenecks that could cause delays or errors.
MCP servers offer huge benefits, but like any powerful system, they need smart planning and strong safeguards to work well in the real world.
The Missing Layer in AI Agent Infrastructure
As AI agents take on more complex tasks—navigating interfaces, executing workflows, and interacting with real-world tools—the need for a reliable control layer has never been greater. That’s where MCP Servers come in: translating intent into action with structure, safety, and context.
Discover how Amazon, Walmart, PlayStation, and other leading teams are building smarter, more reliable AI systems at Breakpoint 2025—BrowserStack’s flagship virtual event on May 14-15.
It brings together 20,000+ engineers and developers to discuss AI-powered testing, autonomous workflows, and the future of intelligent testing.
Hear from Anu Bharadwaj (President, Atlassian), Michael Bolton (co-creator of Rapid Software Testing), Joe Colantonio (TestGuild), and others shaping the future of intelligent QA.
Written by David Burns
David is Head of Open Source at BrowserStack and a core contributor to Selenium. He’s passionate about building scalable testing infrastructure and making open-source tooling more accessible to developers and testers worldwide.
