Max Headroom 
This is the last of four parts in this series. Part 1 can be found here, Part 2 here, and Part 3 here.
9. Future Directions and Wishlist for MCP
The trajectory of MCP and AI tool integration is exciting, and there are clear areas where the community and companies are pushing things forward. Here are some future directions and “wishlist” items that could shape the next wave of MCP development:
Formalized security and authentication: As noted, one of the top needs is standard security mechanisms in the MCP spec. We can expect efforts to define an authentication layer—perhaps an OAuth-like flow or API key standard for MCP servers so that clients can securely connect to remote servers without custom config for each. This might involve servers advertising their auth method (e.g., “I require a token”) and clients handling token exchange. Additionally, a permission model could be introduced. For example, an AI client might pass along a scope of allowed actions for a session, or MCP servers might support user roles. While not trivial, “standards for MCP security and authentication” are anticipated as MCP moves into more enterprise and multiuser domains. In practice, this could also mean better sandboxing—maybe running certain MCP actions in isolated environments. (Imagine a Dockerized MCP server for dangerous tasks.)
MCP gateway/orchestration layer: Right now, if an AI needs to use five tools, it opens five connections to different servers. A future improvement could be an MCP gateway—a unified endpoint that aggregates multiple MCP services. Think of it like a proxy that exposes many tools under one roof, possibly handling routing and even high-level decision-making about which tool to use. Such a gateway could manage multitenancy (so one service can serve many users and tools while keeping data separate) and enforce policies (like rate limits, logging all AI actions for audit, etc.). For users, it simplifies configuration—point the AI to one place and it has all your integrated tools.
A gateway could also handle tool selection: As the number of available MCP servers grows, an AI might have access to overlapping tools (maybe two different database connectors). A smart orchestration layer could help choose the right one or combine results. We might also see a registry or discovery service, where an AI agent can query “What MCP services are available enterprise-wide?” without preconfiguration, akin to how microservices can register themselves. This ties into enterprise deployment: Companies might host an internal catalog of MCP endpoints (for internal APIs, data sources, etc.), and AI systems could discover and use them dynamically.
Optimized and fine-tuned AI agents: On the AI model side, we’ll likely see models that are fine-tuned for tool use and MCP specifically. Anthropic already mentioned future “AI models optimized for MCP interaction.” This could mean the model understands the protocol deeply, knows how to format requests exactly, and perhaps has been trained on logs of successful MCP-based operations. A specialized “agentic” model might also incorporate better reasoning to decide when to use a tool versus answer from memory, etc. We may also see improvements in how models handle long sessions with tools—maintaining a working memory of what tools have done (so they don’t repeat queries unnecessarily). All this would make MCP-driven agents more efficient and reliable.
Expansion of built-in MCP in applications: Right now, most MCP servers are community add-ons. But imagine if popular software started shipping with MCP support out of the box. The future could hold applications with native MCP servers. The vision of “more applications shipping with built-in MCP servers” is likely. In practice, this might mean, for example, Figma or VS Code includes an MCP endpoint you can enable in settings. Or an enterprise software vendor like Salesforce provides an MCP interface as part of its API suite. This would tremendously accelerate adoption because users wouldn’t have to rely on third-party plug-ins (which may lag behind software updates). It also puts a bit of an onus on app developers to define how AI should interact with their app, possibly leading to standardized schemas for common app types.
Enhanced agent reasoning and multitool strategies: Future AI agents might get better at multistep, multitool problem-solving. They could learn strategies like using one tool to gather information, reasoning, then using another to act. This is related to model improvements but also to building higher-level planning modules on top of the raw model. Projects like AutoGPT attempt this, but integrating tightly with MCP might yield an “auto-agent” that can configure and execute complex workflows. We might also see collaborative agents (multiple AI agents with different MCP specializations working together). For example, one AI might specialize in database queries and another in writing reports; via MCP and a coordinator, they could jointly handle a “Generate a quarterly report” task.
User interface and experience innovations: On the user side, as these AI agents become more capable, the interfaces might evolve. Instead of a simple chat window, you might have an AI “dashboard” showing which tools are in use, with toggles to enable/disable them. Users might be able to drag-and-drop connections (“attach” an MCP server to their agent like plugging in a device). Also, feedback mechanisms could be enhanced—e.g., if the AI does something via MCP, the UI could show a confirmation (like “AI created a file report.xlsx using Excel MCP”). This builds trust and also lets users correct course if needed. Some envision a future where interacting with an AI agent becomes like managing an employee: You give it access (MCP keys) to certain resources, review its outputs, and gradually increase responsibility.
The overarching theme of future directions is making MCP more seamless, secure, and powerful. We’re at the stage akin to early internet protocols—the basics are working, and now it’s about refinement and scale.
10. Final Thoughts: Unlocking a New Wave of Composable, Intelligent Workflows
MCP may still be in its infancy, but it’s poised to be a foundational technology in how we build and use software in the age of AI. By standardizing the interface between AI agents and applications, MCP is doing for AI what APIs did for web services—making integration composable, reusable, and scalable. This has profound implications for developers and businesses.
We could soon live in a world where AI assistants are not confined to answering questions but are true coworkers. They’ll use tools on our behalf, coordinate complex tasks, and adapt to new tools as easily as a new hire might—or perhaps even more easily. Workflows that once required gluing together scripts or clicking through dozens of UIs might be accomplished by a simple conversation with an AI that “knows the ropes.” And the beauty is, thanks to MCP, the ropes are standardized—the AI doesn’t have to learn each one from scratch for every app.
For software engineers, adopting MCP in tooling offers a strategic advantage. It means your product can plug into the emergent ecosystem of AI agents. Users might prefer tools that work with their AI assistants out of the box.
The bigger picture is composability. We’ve seen composable services in cloud (microservices) and composable UI components in frontend—now we’re looking at composable intelligence. You can mix and match AI capabilities with tool capabilities to assemble solutions to problems on the fly. It recalls Unix philosophy (“do one thing well”) but applied to AI and tools, where an agent pipes data from one MCP service to another, orchestrating a solution. This unlocks creativity: Developers and even end users can dream up workflows without waiting for someone to formally integrate those products. Want your design tool to talk to your code editor? If both have MCP, you can bridge them with a bit of agent prompting. In effect, users become integrators, instructing their AI to weave together solutions ad hoc. That’s a powerful shift.
Of course, to fully unlock this, we’ll need to address the challenges discussed—mainly around trust and robustness—but those feel surmountable with active development and community vigilance. The fact that major players like Anthropic are driving this as open source, and that companies like Zapier are onboard, gives confidence that MCP (or something very much like it) will persist and grow. It’s telling that even in its early phase, we have success stories like Blender MCP going viral and real productivity gains (e.g., “5x faster UI implementation” with Figma MCP). These provide a glimpse of what a mature MCP ecosystem could do across all domains.
For engineers reading this deep dive, the takeaway is clear: MCP matters. It’s worth understanding and perhaps experimenting with in your context. Whether it’s integrating an AI into your development workflow via existing MCP servers, or building one for your project, the investment could pay off by automating grunt work and enabling new features. As with any standard, there’s a network effect—early contributors help steer it and also benefit from being ahead of the curve as adoption grows.
In final reflection, MCP represents a paradigm shift where AI is treated as a first-class user and operator of software. We’re moving toward a future where using a computer could mean telling an AI what outcome you want, and it figures out which apps to open and what buttons to press—a true personal developer/assistant. It’s a bit like having a superpower, or at least a very competent team working for you. And like any revolution in computing interfaces (GUI, touch, voice, etc.), once you experience it, going back to the old way feels limiting. MCP is a key enabler of that revolution for developers.
But the direction is set: AI agents that can fluidly and safely interact with the wide world of software. If successful, MCP will have unlocked a new wave of composable, intelligent workflows that boost productivity and even how we think about problem-solving. In a very real sense, it could help “remove the burden of the mechanical so people can focus on the creative” as Block’s CTO put it.
And that is why MCP matters.
It’s building the bridge to a future where humans and AI collaborate through software in ways we are only beginning to imagine, but which soon might become the new normal in software engineering and beyond.
AI & ML, Research
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