MCP: The Protocol That's Quietly Rewiring How AI Systems Connect
The pretext is simple: we're witnessing the second wave of AI
The first wave was AI wrappers—brilliant but isolated islands of intelligence. Companies like Perplexity, Cursor, and Replit built impressive tools that essentially put a beautiful interface on top of someone else's LLM[1][2][3]. These AI wrappers were initially dismissed as "just an API call with a prompt"[2], but they quietly became multi-billion dollar businesses while the tech world obsessed over foundation models.
Now we're entering something fundamentally different. The second wave isn't about building smarter models—it's about making AI systems talk to each other.
Enter the Model Context Protocol (MCP), Anthropic's open standard that's positioning itself as the "USB-C of AI"[4][5][6]. But is MCP truly the infrastructure that will connect our fragmented AI ecosystem, or just another overhyped protocol destined to become a footnote in tech history?
We are in the midst of a monumental shift, a "second wave" of artificial intelligence that promises to be more powerful and transformative than the first[1]. The initial wave brought us impressive AI "wrappers"—standalone tools and large language models (LLMs) that felt like isolated islands of intelligence. Now, a new tide is rising: agentic AI. This is the next evolution, where AI systems don't just respond to prompts but can reason, plan, and autonomously execute complex tasks[2][3].
At the heart of this revolution is a fringe, yet rapidly growing, technology called the Model Context Protocol (MCP). Pitched as the potential "USB-C of AI," MCP is a standardized framework designed to be the universal glue for this new agentic world, connecting disparate AI agents and workflows[4][5].
But is MCP the key that unlocks the next chapter of AI, potentially leading us toward Artificial General Intelligence (AGI) and autonomous robotics? Or is it merely a piece of technical plumbing—an important building block like TCP/IP, but ultimately just a cog in a much larger machine? Let's explore the bull and bear cases for MCP.
The argument for MCP is powerful and dripping with potential. It positions the protocol not just as an improvement, but as the fundamental catalyst for the next generation of AI.
- The "USB-C" for a Connected AI World: The greatest strength of MCP is its potential to become a universal standard[6]. Before MCP, connecting an AI model to an external tool or database required a custom, often brittle, integration. MCP replaces this mess with a single, secure, and standardized protocol[5]. This means developers can build an AI application (an MCP client) once and have it work with any number of compliant tools and data sources (MCP servers), creating a truly interoperable ecosystem[7][6].
- Unleashing True Agentic Power: Agentic AI has long been a dream, but it was missing a key ingredient: a reliable way to interact with the real world[6]. MCP provides that missing piece. It gives AI agents a standardized "toolbox" to perform concrete actions, moving them beyond simple text generation to orchestrating complex tasks like booking travel, analyzing legal contracts, or managing your inbox[6][8]. Companies are already betting on this future; the merger of Superhuman and Grammarly aims to create an ecosystem of agentic office tools, with email acting as the staging ground for orchestrating multiple AI agents at once[9][10].
- Breaking Through the LLM Ceiling: There's a growing sense that we may be approaching "peak LLM," where simply making models bigger yields diminishing returns[11]. MCP circumvents these limitations by allowing LLMs to access real-time information and take action in the world, overcoming their inherent constraints like knowledge cut-off dates[6].
- The Path to AGI and Autonomous Robotics: MCP is seen as a critical step toward more advanced autonomous systems. In robotics, it provides a flexible framework that bridges intelligent AI decision-making with precise hardware control[12]. This allows robots to dynamically adapt to complex environments, a foundational requirement for the sophisticated, autonomous robots of the future[12][13].
- A New Economic Ecosystem: Just as the iPhone created the app store economy, MCP is fostering a new marketplace for specialized AI tools and services. A flourishing economy of MCP servers is emerging, offering everything from financial data analysis to healthcare-specific tools, allowing developers to build powerful, specialized AI applications faster than ever before[6].
Despite the excitement, a healthy dose of skepticism is warranted. Is MCP truly the revolutionary force its proponents claim, or could it be a footnote in the history of AI?
- Hype vs. Underwhelming Reality: For some, the initial experience with MCP can feel underwhelming. Its true power is unlocked when you build your own custom agents with access to your files and systems—a task that requires significant technical skill and a clear use case[14]. For non-developers or those with simple needs, MCP may seem like a solution in search of a problem[14].
- A Cog in the System, Not the Engine: The most compelling bear case is that MCP, while useful, is simply infrastructure. Like the HTTP protocol that powers the web, it's a vital, foundational layer that enables innovation, but it isn't the innovation itself[6]. Users don't care about HTTP; they care about Google and Netflix. Similarly, users will care about the powerful agentic applications, not the protocol running underneath.
- Competition from RAG and Direct API Training: The rise of other technologies could diminish MCP's importance. Retrieval-Augmented Generation (RAG) allows LLMs to pull from vast internal knowledge stores. If RAG becomes sufficiently advanced, the need for some external data calls via MCP could be reduced. Furthermore, what if future LLMs can be trained to understand and interact with API specifications directly, effectively bypassing the need for a standardized intermediary like MCP?
- The Risk of Complexity: If the tooling around MCP becomes too complex, it could scare away developers. Widespread adoption hinges on simplicity and ease of use. If building and maintaining MCP servers becomes a major engineering challenge, its growth could stall.
While the bear arguments have merit, they seem to miss the bigger picture. The worries about MCP being undermined by RAG or other technologies are likely overblown. RAG and MCP are not competitors; they are complements. RAG helps an AI know what to do, while MCP helps it do it. A truly robust AI system will leverage both—using RAG for intelligent retrieval and MCP for intelligent action.
The reality is that MCP has already captured critical momentum and is widely seen as the presumptive winner in the race to standardize agent-to-tool communication[5]. Its rapid adoption across the industry signals a high degree of confidence in its capabilities[6].
We are moving from an era where the value of AI was in what it knows to a new era where the value is in what it can do. MCP is more than just a protocol; it represents a fundamental paradigm shift in how we build and interact with artificial intelligence[6][12]. It is the key to unlocking the second wave of AI—one defined by interconnected, collaborative, and truly autonomous agents. Companies that fail to recognize this shift and integrate MCP into their roadmaps risk being left behind in a world of isolated, first-wave AI islands. The future is connected, and MCP is the standard that will bind it all together.
So what is MCP? Simply put. MCP makes agentic AI really easy to connect to services and API's.
- Fringe tech growing fast, in a booming AI revolution
- The standard that makes it easy to connect AI to the rest of the world
- AI wrappers was the first wave, MCP connected AI is the second wave
- Another better protocol could make it irrelevant
- MCP is just Websocket at its core, and websocket is finicky to operate
- You still need user registration and auth serviced by a third party
- Agentic coding, agentic workflows are poised to become the next evolution in AI.
- Comanies like Superhuman are merging with companies like grammarly to form new eco system of agentic office tools.
- MCP is positioned to be the glue between agentic usage, be it Agentic flows or agent to agent systems
- Every AI companies has MCP on their roadmap, but is it just a hype, a result of devs needing something to do?
- Are we peak what LLMs can achive, and the rest is just iimplementation with agentic flows and systems.
- Is MCP the path to AGI? and autonomouse robotics?
- Or is MCP simply just a hype, a foot note in history, important building block like TCP or https. But in the end just a cog in the system?
Three massive trends are converging right now:
1. The Agentic Revolution is Real Agentic AI—autonomous systems that can make decisions and perform tasks without human intervention—has moved from academic curiosity to business reality[4][5]. Companies are deploying AI agents that can write code, manage workflows, and solve complex problems independently[6][7].
2. The Great AI Merger Wave We're witnessing unprecedented consolidation in the AI productivity space. Grammarly's acquisition of Superhuman for its AI-powered email platform signals a new era of agentic office ecosystems[8][9]. These aren't just feature additions—they're foundational shifts toward AI agents that collaborate across multiple communication and productivity tools.
3. The Infrastructure Gap The biggest bottleneck isn't AI capability anymore—it's connectivity. How do you get your coding agent to talk to your email agent? How do you create workflows where multiple AI systems work together seamlessly? This is where MCP becomes the critical infrastructure layer[1][10].
Think about the early days of the internet. Computers existed, but they couldn't talk to each other. Then came TCP/IP—not flashy, not exciting, but absolutely foundational[7][8][9]. TCP/IP enabled email, web browsing, and every digital service we use today by creating a universal language for networked communication.
MCP is having its TCP/IP moment right now[7][8][10]. Just as TCP/IP connected isolated computers into the internet, MCP is connecting isolated AI systems into something far more powerful.
The numbers tell the story. Since its release in November 2024[11], MCP has exploded:
- 1,000+ MCP connectors built by the community in just months[12]
- Major adoption across Big Tech: OpenAI, Google DeepMind, Microsoft, Amazon Web Services, and Cloudflare all now support MCP[13][14]
- Enterprise momentum: Companies like Block report 50-75% time savings on common tasks using MCP-driven systems[12]
The first wave of AI created what we call "isolated islands"—brilliant but disconnected tools[15]. Your AI coding assistant couldn't access your CRM. Your customer service AI couldn't update your database. Each AI wrapper lived in its own silo, requiring custom integrations that cost millions and took months to build[16][8].
MCP changes this fundamentally. Instead of the M×N problem (connecting M AI models to N data sources requiring M×N integrations), MCP reduces this to M+N[17][6]. One protocol, infinite possibilities.
We're not just talking about better chatbots. Agentic AI systems—AI that can reason, plan, and act autonomously across multiple systems—are already being deployed at scale[18][19][20]:
- Autonomous banking agents that handle transaction disputes end-to-end, from filing complaints to issuing provisional credits[21]
- Enterprise workflows where AI agents coordinate across dozens of systems, updating CRMs, sending emails, and scheduling meetings without human intervention[7]
- Supply chain optimization where AI agents predict disruptions and automatically reorder inventory[22]
Companies like Superhuman are already merging with companies like Grammarly to form new ecosystems of agentic office tools[cited in query]. MCP is positioned to be the glue between these agentic workflows, enabling agent-to-agent communication at unprecedented scale.
Here's where it gets really interesting. Some argue that AGI isn't about building one superintelligent model—it's about connecting existing intelligent systems[23]. The Reddit community on r/singularity suggests that "transformer models + tool calling + database retrieval/memory" could be equivalent to AGI[23]. MCP enables exactly this architecture.
Consider the broader implications:
- Robotic control systems using MCP to coordinate AI agents with sensors, cameras, and actuators in real-time[24][25][26]
- Multi-agent research teams where AI systems collaborate across disciplines to solve complex problems[27]
- Autonomous cities where thousands of AI agents manage traffic, utilities, and services through standardized MCP connections
Before we get carried away with the MCP hype, let's talk about the elephant in the room: security. MCP essentially allows AI systems to execute code, access databases, and perform actions across your entire digital infrastructure[28][29]. What could go wrong?
The security challenges are staggering:
- Prompt injection attacks where malicious users trick AI into executing unauthorized commands[28][29]
- Tool permission escalation where combining tools can lead to unintended data access[28]
- Authentication gaps as current MCP implementations lack enterprise-grade security measures[30][31]
Microsoft's security team warns that "MCP lacks built-in server protection and essential security measures required for enterprise-grade generative AI solutions"[28]. We're essentially giving AI systems the keys to the kingdom before we've figured out how to properly lock the doors.
Here's an uncomfortable truth: most of what MCP promises to do, RAG (Retrieval-Augmented Generation) already does—and does it more safely[32][33][34].
RAG has been battle-tested in production for years. It's:
- More cost-effective than MCP's real-time tool calling[32][35]
- Easier to implement for most enterprise use cases[34]
- More secure by design, as it doesn't require giving AI systems direct access to external tools[32]
As one industry analysis notes: "RAG continues to be beneficial for tapping into extensive, regularly updated knowledge repositories and integrating with outside systems"[33]. Why fix what isn't broken?
The current MCP landscape reveals troubling cracks in the foundation:
- Two incompatible specs (v1 from 2024 and v2 from 2025) with zero public clients supporting the newer version[36]
- Role separation problems where the same person must configure and use MCP servers, creating operational bottlenecks[36]
- Deployment nightmares with most MCP servers designed for single-tenant use, making enterprise scaling challenging[31]
Enterprise CTOs are approaching MCP with caution. As Rocket Companies' CTO notes: "We prefer to wait for more critical mass before embracing it in production"[13]. Smart money is waiting on the sidelines.
Here's a deeper technical concern: MCP might be too low-level to achieve widespread adoption[37]. As one industry observer notes, "if MCP is the TCP/IP, where's the HTTP?"
TCP/IP succeeded because higher-level protocols like HTTP made it accessible to developers. MCP currently feels "raw" and requires significant technical expertise to implement properly[37]. Without a more accessible abstraction layer, MCP might remain a tool for AI engineers rather than becoming the universal standard it aspires to be.
LLM reliability often negatively correlates with the amount of context and instructions provided[38]. MCP, by design, increases both context and system complexity. Every new MCP server adds:
- More potential failure points
- Additional latency in AI responses
- Increased cognitive load on the LLM
- More opportunities for unexpected interactions between tools
We might be building systems that are technically impressive but practically unreliable.
There's growing evidence we may be approaching peak LLM capabilities[11]. GPT-5 might not be exponentially better than GPT-4. The future isn't about smarter individual models—it's about smarter systems of models working together.
MCP enables this by allowing AI agents to:
- Access real-time information beyond their training cutoffs[12]
- Perform concrete actions in the real world[12]
- Coordinate with other AI systems seamlessly[10]
MCP isn't just for software. It's already being deployed in robotic control systems[13][14], where AI agents need to coordinate between sensors, actuators, and decision-making systems. The protocol's ability to handle contextual adaptability and multi-system integration makes it a natural bridge to autonomous robotics[13].
Imagine autonomous vehicles where the navigation AI, safety AI, and communication AI all coordinate through MCP. Or humanoid robots where dozens of specialized AI agents work together seamlessly[14].
MCP adoption is showing classic network effect patterns[15]. Major companies are building MCP servers, and the ecosystem is expanding rapidly[12]. When a protocol achieves critical mass—like HTTP did for the web—it becomes nearly impossible to dislodge.
As one industry observer noted: "MCP has captured enough critical mass and momentum that it is already the presumptive winner of the 2023-2025 'agent open standard' wars"[15].
Retrieval-Augmented Generation (RAG) systems are becoming incredibly sophisticated at knowledge management. As LLMs get better at internal knowledge storage and RAG systems become more efficient, the need for external tool integration through MCP might diminish significantly.
Why build complex MCP integrations when you can just train the model on API specifications or store the information directly in vector databases?
Early MCP implementations are revealing significant complexity challenges[16]. Developers are reporting "underwhelming" experiences with confusing targeting and difficult setup processes[16].
The history of technology is littered with "universal standards" that were too complex for widespread adoption. MCP might be technically superior but practically unusable for most developers.
Maybe MCP succeeds, but in the way TCP/IP succeeded—as critical infrastructure that becomes completely invisible. TCP/IP was revolutionary, but it didn't make anyone rich who invested in "TCP/IP companies." It just became plumbing.
MCP might be the same: important, essential, but ultimately just foundational infrastructure that everyone takes for granted.
Here's what makes me bullish despite the valid concerns:
The timing is perfect. Unlike previous "universal AI standard" attempts, MCP arrives at the exact moment when:
- AI capabilities are mature enough to be truly useful
- The complexity of multi-agent systems demands standardization
- Enterprise adoption is accelerating rapidly
The ecosystem momentum is real. When you see companies like Make.com building MCP servers[7] and major acquisitions like Grammarly-Superhuman specifically mentioning AI agent collaboration[8][9], this isn't just hype—it's infrastructure investment.
The alternative is chaos. Without MCP or something like it, we're heading toward a fragmented ecosystem of incompatible AI agents. The market will demand interoperability, and MCP is positioned to provide it.
Whether MCP becomes the foundation of AGI or just invisible plumbing, one thing is certain: the second wave of AI is fundamentally different from the first. We're moving from isolated AI tools to collaborative AI ecosystems.
The companies, developers, and investors who understand this shift—and position themselves accordingly—will capture disproportionate value in the coming years.
The question isn't whether MCP will matter. The question is whether you'll recognize its importance before everyone else does.
The future of AI isn't about building better models. It's about building better systems. And systems need standards.
MCP might just be that standard.
What do you think? Are we witnessing the birth of the USB-C for AI, or just another overhyped protocol that will fade into obscurity? The next 18 months will tell us everything.
So is MCP the future of AI or just another overhyped protocol?
The bull case is compelling: we're seeing unprecedented cross-industry adoption, real enterprise value creation, and the emergence of truly autonomous AI systems. MCP could indeed be AI's TCP/IP moment—the infrastructure layer that enables the next phase of intelligent automation.
But the bear case is sobering: security challenges are massive, alternatives like RAG work well for most use cases, and enterprise adoption remains cautious. MCP might be a solution in search of a problem, or worse, a protocol that introduces more complexity than value.
Here's what I believe: MCP represents a fundamental bet on the future of AI. If you believe that the future of AI is agentic systems working together autonomously, then MCP is essential infrastructure. If you believe that AI will remain primarily assistive tools with human oversight, then MCP is probably overkill.
The stakes couldn't be higher. Companies that bet correctly on MCP's trajectory will build the next generation of AI-native businesses. Those that bet wrong will either over-invest in unnecessary complexity or miss the boat on the biggest infrastructure shift since the cloud.
My prediction? MCP will succeed, but not in the way most people expect. Like many foundational technologies, its biggest impact will come from use cases we haven't imagined yet. The question isn't whether MCP will be important—it's whether you'll be ready when it becomes obvious to everyone else.
What's your take? Are we witnessing AI's TCP/IP moment, or is this just another case of Silicon Valley getting carried away with its own hype? The future of AI connectivity might depend on how we answer this question.
This analysis is based on extensive research of industry reports, technical documentation, and expert interviews. For more deep dives into emerging AI infrastructure, subscribe to stay ahead of the curve.
