Picture this: You’re working on an internal AI assistant to help triage support tickets. It needs to fetch customer history from a CRM, suggest knowledge base articles and escalate issues through company chat. The problem is, each task requires a custom integration, an API shim or a brittle script that breaks the moment a vendor changes an endpoint.
Sound familiar?
For years, developers have lived in this fragmented reality, cobbling together brittle connections between systems, each integration a bespoke artifact. But that may be about to change.
The Model Context Protocol (MCP) is an emerging open standard developed by Anthropic and now adopted by major industry players, and it’s simplifying how AI models interact with external tools and data.
It’s a deceptively simple idea. Like many transformative shifts in computing, such as HTTP or REST, its power lies in its ability to create a universal surface for connection. In that way, MCP is more than just another protocol. It’s a platform primitive for the AI-native era.
The AI integration problem nobody talks about
AI systems today are incredibly capable. They can draft emails, debug code and translate languages, but they typically work in a vacuum. Getting them to operate meaningfully within real-world systems requires a patchwork of glue code, custom prompts and human supervision.
It’s inefficient, and a barrier to innovation.
Suppose your AI assistant needs to:
Pull current sales figures from a business intelligence dashboardSearch support docs for a known issueCall a service to initiate a product return for a customer
Even if each system has a well-documented API, your model doesn’t “understand” how to use them. Developers must build complex retrieval pipelines or create brittle function-calling wrappers. And that’s assuming the model even has access to those tools in the first place.
What if you flip the script? What if the systems told the model what tools are available, what they do, how they work and what kind of data they accept? That’s exactly what MCP does.
MCP is a protocol for shared context
At its core, Model Context Protocol provides a structured way for a system to expose its capabilities to language models and other generative AI models. This includes:
Tools: Functions that the model can call (for example, lookup_customer_by_email)Resources: Structured data a model can reference (for example, product catalog, user records)Prompt templates: Pre-written prompts the system can use to guide model behavior (for example, “Summarize this customer’s sentiment history”)
Think of MCP as a contract. It’s a way for an external system to declare what it can do and how you can talk to it. All of this is described in a machine-readable way so that models, whether from Anthropic, OpenAI, Meta or elsewhere, can understand.
Why it feels a lot like Kafka (in a good way)
In traditional software architecture, Apache Kafka acts as a central nervous system. It decouples producers and consumers, allowing systems to communicate with a standardized event stream. You don’t care how the producer made the event or what the consumer does with it. As long as both speak Kafka, things work. MCP serves a similar role, but for AI interaction.
Instead of event logs, it exposes context (such as tools, resources and prompts) in a standardized schema that models can interpret and invoke. It becomes a substrate, a kind of universal interface for cognitive operations, letting tools be composed like building blocks.
And just like Kafka helped usher in the modern data stack, MCP could help build the modern AI stack, one where every tool, every system, every dataset is natively usable by an AI model with minimal glue.
Real-world example: From assistant to analyst
Let’s bring this to life. Suppose you’re building an AI assistant for a cybersecurity team. With MCP, you could expose a handful of tools:
query_threat_db(ip: str): Look up known malicious indicators summarize_log(file: str): Provide a high-level overview of suspicious activitytrigger_incident(response: str): Initiate a playbook
You also publish resources like the team’s on-call calendar and recent vulnerability reports, and define a few prompt templates for escalation language or ticket filing.
Now, when the analyst asks whether a specific IP has shown up in any previous reports, the AI assistant doesn’t guess. It calls the tool. When the analyst asks for a summary of the logs, the assistant doesn’t hallucinate, and instead uses a defined prompt.
This isn’t just convenience, it’s the difference between AI as a novelty and AI as a teammate.
The expanding ecosystem
The most exciting thing about MCP isn’t just its technical elegance, it’s the momentum behind it. As of April 2025, MCP has official or in-progress support from:
Anthropic, who created it and uses it in ClaudeOpenAI, integrating MCP into ChatGPT and the Agents SDKMicrosoft, supporting it in Copilot Studio and contributing a C# SDKTooling platforms like Replit, Cursor, Sourcegraph and Zed
And because the protocol is open and platform-agnostic, it’s becoming the common language for anyone building LLM-powered systems, from the solo developer writing Python scripts to the enterprise architect managing dozens of AI-enabled workflows.
Glimpse into the future of AI
It’s not hard to imagine where this could go. We may soon see:
Tool marketplaces, where MCP-enabled tools can be discovered and shared across organizations.Versioned tool contracts, ensuring backward compatibility as APIs evolve.Security layers and permission schemas, so models can only access what they’re supposed to.Tool chaining, where models compose MCP tools into workflows without human prompts.
Eventually, this might all just be assumed, the way HTTP is. You won’t think about “MCP integration” any more than you think about TCP/IP when you open your browser. You’ll just expect that your model can “see” and “use” the tools you’ve made available.
That’s the real promise of MCP: Not just making AI smarter, but making it actually useful where it matters most.
Closing thoughts
We’re at an inflection point. The early days of AI were focused on capability, finding out how much a model could do. The next phase is about connectivity, and how well it fits into our existing systems, workflows and expectations.
MCP is a subtle shift, but a profound one. It turns AI from a black box into a platform citizen. And for those of us building the next generation of software, it offers something we haven’t had in a long time: A standard we can build on.
But there’s a crucial detail that’s easy to overlook: MCP is a specification, not an implementation. That means trust, reliability and security aren’t baked into the protocol itself, but depend entirely on how it’s deployed.
As the number of MCP servers grows (hundreds already exist), the ecosystem must grapple with issues of trust, server provenance and secure execution. Who’s running your MCP server? Can you trust it? Should your model trust it?
This is exactly where open source shines. Transparent, community-audited MCP servers give developers a fighting chance to verify what their systems are actually doing — not just what they’re told. Security by design becomes possible when implementation details are visible, testable and collectively improved.
In other words, MCP sets the rules, but the players matter. And if we want this future to be as powerful as it is promising, we need to invest not just in the protocol, but in trusted, open implementations that uphold its spirit.
