Search & AI Engineer at KMW Technology
Model Control Protocol (MCP) in LLM Apps: Overkill or Integral?
As large language models (LLMs) become more central to app development, we’re starting to see a surge in new “standards” that aim to streamline the way we work with them. One of those is Model Control Protocol (MCP)—a specification that introduces structured patterns for how LLMs can interact with external tools and services.
As LLM use evolves, MCP has the potential to meet a real need: a consistent interface, reusable patterns, well-defined roles for inputs and tools. After trying it in a few real-world projects, I’ve landed on a pretty firm opinion:
MCP is overkill for some self-contained LLM applications.
Let’s break that down.
WHAT MCP Tries to SOLVE
MCP offers a standardized format for:
- Defining tools and functions an LLM can use
- Structuring prompts and inputs
- Managing model responses
- Integrating external systems into a unified orchestration layer
If you’re building a platform where multiple apps or teams need to access a model with consistent behavior, then yes, MCP can be helpful. It makes your model “discoverable” and “programmable” in the same way REST and GraphQL made APIs predictable.
But here’s the catch…
In self-contained apps, it’s just extra weight
If you’re building an app where you own the model, you control the tools, and you define the prompt context—you don’t need MCP. You already know what the LLM needs. You’ve got the orchestration baked into your app’s API layer.
Let’s take a typical modern setup:
- A Next.js app with built-in API routes
- A custom orchestrator to manage model calls and tool routing
- Locally hosted context (embeddings, history, memory)
- Internal tools for summarization, tagging, or database lookup
In this environment, introducing MCP means:
- Creating new wrappers around tools
- Translating your existing structure into a generic spec
- Debugging new abstraction layers
- Maintaining protocol compliance on top of your working logic
All of which gives you… what? A clear contract between the LLM and the MCP service?
That’s not simplification. That’s ceremony.
Where MCP Does Make Sense
MCP shines when you’re not the only one calling the model. For example:
- Multi-tenant LLM services that need a consistent interface for different consumers
- Third-party apps that interact with a centralized orchestration layer
- Teams with shared infrastructure, where models and tools are exposed through a shared gateway
In those cases, it’s worth it. Standardization reduces friction. It enables reuse. It helps with governance, monitoring, and security. MCP becomes a contract between producers and consumers of LLM capability.
But most apps aren’t there. Most LLM-enabled apps today are still early, experimental, and owned end-to-end by a single team. In those scenarios, MCP’s benefits are marginal.
The Real Game-Changer: Function Calling
What’s actually changing the landscape isn’t protocol—it’s capability.
LLMs are getting much better at tool calling (aka function calling). With structured outputs, schema validation, and multi-step reasoning, we’re approaching the point where your model can:
- Choose the right tool on its own
- Execute functions with arguments
- Chain results intelligently
- Ask follow-up questions when needed
That’s the future. And it’s pretty easy to imagine a world where a new generation of LLMs are trained and tuned for MCP based tool use, making this protocol even more powerful.
We are already seeing existing application providers looking to expose their data and services via MCP to hook into AI applications more easily. There are certainly benefits to adopting early in order to play nice in a wider AI ecosystem.
Final Thought
Is MCP overkill or integral? As with any tech decision, it depends. As LLM development matures, we’re moving toward simplicity, agility, and tight integration and each layer of protocol is going to need to justify its own weight. At the same time, we are now seeing the next wave of LLM applications where external integration and tool calling are absolutely essential. At KMW, we are watching MCP closely and experimenting with more complex tools while keeping a close eye on industry adoption of the standard. These are exciting times in AI development and we’ll have more to come soon
TL;DR
- MCP is useful when you’re exposing models to external consumers or services and need a standardized interface.
- It’s overkill for internal, self-contained apps where you already manage the model’s full context and environment.
- Function calling and tool use are the real potential for MCP, and they are only going to gain in importance and capability going forward.
- Assess whether the complexity is warranted, and if your use case actually requires it. Your LLM doesn’t need a passport to move around in its own country.
