A few months ago, I had a workflow that should have been simple. Our AI assistant needed to check a customer's subscription status in our database, then look up their recent support tickets, then decide whether to flag the account for a review. Three systems, three different APIs, three different authentication methods.
Building that workflow took a week. I spent three days just on authentication and data format conversion. The actual business logic was maybe a few hours of work. The rest was plumbing.
That experience is why I started paying attention to MCP, which stands for Model Context Protocol. It's a standardized way for AI systems to interact with external tools and data sources. Instead of building custom integrations for every tool, you build to a common interface that any MCP-compatible system can use.
After building two production MCP servers and integrating with several others, here is what I have learned about how this technology actually works and where it still has room to grow.
What MCP Actually Is
The simplest way to understand MCP is to compare it to USB. Remember when every device had its own special cable and its own special adapter and you needed a specific charger for each thing? USB simplified that by providing a common connector and a common protocol. Now you can plug almost any device into almost any port and have it work.
MCP is trying to do for AI tool calling what USB did for hardware connectivity. Instead of every AI system defining its own format for tool descriptions, tool parameters, and responses, MCP provides a standard that everyone can follow.
From a practical standpoint, an MCP server is a program that exposes two kinds of capabilities. Resources are data that the AI can read, like files or database records. Tools are actions the AI can trigger, like sending a message or updating a record. Both use a standard format for describing what they do and what parameters they need.
The AI application acts as an MCP client. It connects to servers, discovers what capabilities are available, and decides when to use them based on what the user is asking for.
Why This Matters for Real Workflows
In a world without MCP, integrating AI with your existing tools requires custom code for each integration. If you want your AI assistant to interact with Slack, your CRM, your database, and your project management tool, you need four separate integrations. Each one requires understanding a different API, handling different authentication schemes, and dealing with different response formats.
With MCP, you still need someone to build the integrations, but the interfaces are standardized. Once an MCP server exists for a tool, any MCP-compatible AI client can use it. The Slack MCP server that works with one AI assistant will work with another. This creates network effects that compound as more tools get MCP support.
The practical benefit for developers is that you can focus on building the actual integration logic rather than figuring out how to expose it to an AI. The protocol handles the communication layer. You just implement the tools.
Building Your First MCP Server
The best way to understand MCP is to build something with it. Here is the basic pattern for creating an MCP server that exposes a simple tool.
The server defines what tools it offers using a structured format that MCP clients can understand. Each tool has a name, a description of what it does, and a specification of its parameters. The server then waits for requests from clients.
When a client wants to use a tool, it sends a request with the tool name and parameters. The server executes the tool logic and returns a structured response. The client then uses that response to formulate its answer to the user.
The key advantage of this pattern is that the tool logic is isolated from the AI logic. You can test your tool logic independently of the AI client. You can swap out AI clients without changing your tool implementations. You can even expose the same tools through different interfaces, like a web dashboard or a command-line interface.
Where MCP Works Well
I have found MCP particularly valuable for workflows that involve multiple systems and require consistent handling. For example, a customer onboarding workflow might need to create an account in your database, send a welcome email, add the customer to your billing system, and create initial project files in your document storage.
With MCP, each of these steps is a tool that the AI can call in sequence. The AI can handle the orchestration logic, deciding which tools to call based on what the user requests, while each tool handles the specifics of its own system.
Another place where MCP shines is for building AI assistants that need access to real-time information. An MCP server can provide access to live data from your systems, so the AI is working with current information rather than relying on training data that might be outdated.
The protocol also handles authentication cleanly. Each MCP server manages its own authentication with its underlying system, and the AI client doesn't need to know the details. This makes it easier to secure each integration properly without centralizing credentials.
Where MCP Gets Complicated
MCP is not without its challenges. Here are the areas where I have run into friction.
Tool selection ambiguity happens when the AI has multiple tools that could handle a request and picks the wrong one. This is more likely when tool descriptions are vague or when the AI doesn't have enough context to make a good choice. Careful tool description writing and providing the AI with relevant context helps, but it doesn't eliminate the problem.
Latency stacking occurs when a workflow requires multiple sequential tool calls. Each call adds latency, and when you have a chain of ten calls, the total time can become noticeable to users. I have addressed this by batching independent calls where possible and by setting appropriate expectations with users about workflows that require multiple steps.
State management is another challenge. MCP tools are designed to be stateless, which makes them easier to test and reason about. But real workflows often need to maintain state across multiple steps. I handle this by designing prompts that maintain context and by storing intermediate results in a way that the AI can reference in subsequent calls.
Error handling requires care. When a tool fails, the AI needs to understand what happened and decide how to proceed. If the error message is just "tool execution failed," the AI doesn't have much to work with. I have learned to make error responses structured and informative, describing what went wrong in terms that the AI can use to decide on a recovery strategy.
Production Considerations
If you are deploying MCP servers in production, there are a few things worth thinking through.
Timeouts matter more than you might expect. Some tool calls look fast but can hang if the underlying system is slow or unavailable. Set reasonable timeout limits and have fallback behavior when calls exceed those limits. Users would rather get a fast error than watch a spinner for thirty seconds.
Observability is essential. Log all tool calls with timing information, parameter summaries, and outcomes. When something breaks at 2am, you will want this data to understand what happened. I use structured logging that makes it easy to search and analyze tool behavior patterns.
Security boundaries need thought. MCP servers typically have credentials for the systems they connect to. These credentials should be scoped as narrowly as possible. A server that only reads customer data shouldn't have write access, even if the MCP protocol would allow it.
The Ecosystem Today
The MCP ecosystem is still developing. A growing number of popular tools have MCP servers available, and more are being added regularly. The protocol itself is stable enough for production use, though it is still evolving as the community learns what works.
The tooling around MCP is improving. Server frameworks make it easier to build compliant servers. Client libraries handle the protocol details so you can focus on your application logic. Documentation is getting better as the user base grows.
My sense is that MCP is at about the stage where REST APIs were in 2005. It works, people are using it in production, but the best practices are still being figured out. If you are building AI systems that need to interact with external tools, now is a good time to start learning and experimenting.
The teams that invest in understanding MCP now will be ahead when the tooling matures and the ecosystem reaches critical mass. In the meantime, you get the benefit of standardized tool calling for the workflows you are building today.