This article describes the differences between Model Context Protocols (MCPs) and AI Agents, two complementary building blocks of AI solutions. It explains the strengths of each, when to use them individually, and how to combine them to build more capable LLM-powered solutions.
As large language models (LLMs) move from being a novelty to becoming a core part of every development, developers face a key architectural choice: how should the model interact with the rest of my system? Two powerful paradigms have emerged in recent years: model context protocols (MCPs) and AI agents.
They seem similar in that they both allow LLMs to "do things", but they couldn't be more different in how they work and where each shines.
If you're building an AI-driven product and struggling to decide which approach to use or how to combine them, this article might help you make the decision.
Model context protocols are basically a system that handles interactions with an LLM based on a schema; they are structured and predictable. MCPs constrain the model within a well-defined interface, much like an API.
The model is told precisely what it should extract, generate, or transform, and is given a schema or protocol to follow. No reasoning loops or decision making. Just a controlled context and structured results.
An MCP is composed of a few basic components that define the form and scope of a model's interaction:
Resources: These are structured pieces of information or entities that the model will use as context. A resource can be a document, information stored in our database, or a schema.
Instructions: Clear directives in natural language that describe what the model should do with the given context. For example, "Extract customer name, complaint type, and sentiment" or "Assign Jorge to the Ensolvers project." These instructions are translated into parameters needed to execute actions in our backend.
Output schema: A strict format that defines the expected output of each tool. It is often enforced using a JSON schema.
Tools (optional): Small functions that the model can call within the protocol. However, in most pure MCP cases, the model does not invoke tools dynamically (like an agent would do); everything is predefined.
What makes MCPs powerful is that they are not freeform. The developer defines the complete interaction protocol in advance and wraps the model in deterministic behavior.
MCPs have a very structured definition, which is why they are considered “plug and play,” with more LLMs having connectors to start integrating them.
Here is a straightforward MCP example:
Agents, on the other hand, are all about reasoning and autonomy; they decide, step by step, how to get a response. It may search for information, call tools, ask follow-up questions, and loop back if needed.
Unlike an MCP, an agent is not only meant to execute actions or retrieve information, but is also meant to find the solution to the input using the available tools.
Agentic frameworks like LangChain take this concept to the next level, helping developers enable large language models (LLMs) to "think" and act more like problem solvers rather than being limited to a single input-output exchange. These frameworks allow LLMs to plan, take action, and adapt based on the current context.
One of LangChain's most powerful features is the ability to conditionally chain steps. This means that the LLM isn't limited to following a rigid script but can decide what to do next based on what just happened. For example, an agent might first try to answer a question with the information it has in its “memory” (also, a framework’s feature, in real terms, is just generated pieces of text based on previous interactions). If it can't, it might decide to use a tool that accesses that information from the database, call an API, or ask the user for further clarification. These decisions are made in real time, and each step in the chain can influence the next.
This conditional logic is essential for building complex workflows such as:
By combining reasoning, memory, tools, and conditional flows, agentic frameworks enable LLMs to behave more like intelligent collaborators than mere static responders.
For structured and repetitive operations, MCPs (Model Context Protocols) are the ideal choice due to their predictability and precision. When dealing with multi-step workflows where the context is more ambiguous or user-defined, agents offer the necessary flexibility and reasoning capabilities.
In situations where structure is critical, or where we only want to give an LLM some capabilities to act on or read from our system, MCPs should be preferred to ensure bulletproof execution. When the workflow includes both structured and unstructured elements, the combination of MCPs and agents offers the best of both worlds.
You don't have to choose between Agents and MCPs; the most robust LLM systems today combine both. Agents handle high-level reasoning and task organization. MCPs intervene in well-defined operations.
Think of the Agent as the brain of the operation: it understands what the user wants, decides the steps, and delegates each task to the appropriate MCP tool, which executes it with consistency and structure.
Let's suppose the user requests:
"Send me a daily update with the current time."
A hybrid flow would be as follows:
Agent: Interprets the request. This is a task that requires precise data and formatting, so it will first obtain the data:
MCP (get_current_time): Obtains the current time using a deterministic tool.
Agent: Compiles the message for that time.
MCP (send_email): Sends the final message to the user's email.
To be practical, let's pretend we have an MCP server running these tools, the example from before:
Since we are using the FastMCP Python package from LangChain, we already have the /mcp endpoint defined. GET operation will provide the discovery information of the MCP, while POST will be for tool execution.
So our simple agent would look something like this:
First, we fetch the tools available on the MCP, then we build the agent with them. /mcp provides the whole structure and description on each tool and the required parameters for our agent to look at those descriptions to know what tool to use and how
MCPs and agents aren't competitors; they're teammates. Knowing when to let the model "think" and when to enclose it in a structure is the key to building fast, secure, and flexible AI systems.
In a sense, the future isn't just about improving LLMs. It's about more brilliant orchestration, and that's why developers will continue to be a key part of building AI solutions, or any other kind of solution, with the AI as a complement.
So don't fall into the trap of using Agents when it is not necessary or forcing yourself to try to have MCPs for everything; you can combine them and build better, more capable, and more intelligent solutions.