Large Language Model Interaction Paradigms
What is the difference between Function Calling, Tools & MCP? And when should they be used?
As Language Models expand in scope and functionality, the way we build with Language Models (LMs) are increasing in complexity.
The challenge is that as the complexity increases, the functionality can only be simplified to some extent, after which the complexity needs to be understood and embraced.
In this article I attempt to compare the different Language Model interaction paradigms, in the footer of the article are a number of deep-dive pieces I wrote on each of the functionalities; Functions, AI Agent Tools and MCP (Model Context Protocol).
Just a caveat, invariably articles like this solicit quite a bit of discussion on what exactly each of these terms mean; the baseline for me is to write the simplest piece of code possible illustrating the technology. That is the baseline and serves as an illustrative benchmark.
The diagram above shows the progression from simpler to more complex approaches, with each offering different levels of autonomy, integration complexity, and potential capabilities.
Functions, AI Agents & MCP
Functions, AI Agents & Model Context Protocol (MCP) are emerging as indispensable tools for building intelligent and efficient LLM-based systems and applications.
Functions
When using function calling, the model itself does not run the functions.
Instead, it generates parameters for potential function calls.
Your application then decides how to handle these parameters, maintaining full control over whether to call the suggested function or take another action.
Functions are structured mechanisms that enable Language Models to structure data for specific integrations.
I consider functions as a data format layer for specific API’s where the language model detects which API is appropriate.
Functions do not interact with APIs, but they do detect which API is appropriate from a pre-defined list & structure the data in the correct format for that API.
AI Agents & Tools
Autonomous software entities powered by LLMs, AI Agents combine reasoning, decision-making, and action-taking to solve complex problems.
They can plan, adapt, and interact with environments, making them ideal for dynamic, multi-step workflows.
AI Agents have access to a pre-defined list of tools which serves as the integration points through which the AI Agent interacts with the outside world.
The AI Agent chooses which tools to use when, orchestrating the tools into a sequence of events to solve a complex task.
Model Context Protocol (MCP)
A protocol for managing and maintaining context in AI Agent interactions, MCP ensures AI Agents retain relevant information across conversations or tasks. By structuring context, MCP enhances coherence, reduces errors, and supports seamless user experiences.
Together, these tools transform Language Model based applications into versatile, context-aware systems that deliver real-world impact.
When to Use Functions, AI Agents, and MCP?
Functions, AI Agent tools & MCP excel in scenarios where Language Models need to move beyond open-ended dialogue.
Here’s when to leverage them:
Structured Data Extraction with Functions
Need to extract specific details — like customer names, product descriptions, or event dates — from unstructured text?
Functions enable Language Models to parse and organise data with precision.
For example, a function can extract order details from a customer email and structure the detail in the correct format ready for submission to a CRM system; streamlining operations.
Again, functions do not directly interact with APIs, but they do detect which API is appropriate from a pre-defined list, and structure the data in the correct format for that API.
Autonomous Task Automation with AI Agent Tools
AI Agents excel in automating complex workflows, like scheduling meetings or generating analytical reports.
By using tools for specific integrations AI Agents can operate independently, saving time and reducing errors.
Model Context Protocol (MCP) should be used when dealing with complex tasks that benefit from specialised AI Agent expertise working together.
It’s ideal for scenarios requiring diverse capabilities that no single AI Agent can efficiently handle alone.
MCP excels in environments where coordination and knowledge transfer between specialised AI Agents.
Aware Reasoning with MCP & AI Agents
For tasks requiring sustained reasoning or adherence to rules, MCP ensures the LLM retains critical context, while AI Agents handle adaptive decision-making.
For instance, an AI Agent managing a project timeline can use MCP to recall prior discussions and functions to update task statuses, delivering consistent outcomes.
Functions, AI Agents, and MCP are reshaping AI by turning LLMs into proactive, context-aware systems. Here’s why they’re essential:
- Precision and Consistency: Functions and MCP minimise ambiguity, ensuring accurate outputs and coherent interactions. A big part of this is the creation of context.
- Autonomy and Flexibility: AI Agent Tools enable independent problem-solving, adapting to evolving user needs or environments.
- Scalability: By integrating with external systems and managing context, the three powers enterprise-grade solutions.
- User-Centric Design: Together, they deliver dynamic, contextually relevant interactions that enhance user satisfaction.
Not every task demands all three
For simple, creative tasks basic prompting may be enough.
Use functions, AI Agents and/or MCP for scenarios requiring structure, autonomy, or sustained context.
Best Practices for Implementation
To maximise the impact of functions, AI Agent Tools and MCP:
- Clarify Roles: Define specific tasks for functions, AI Agent Tools and MCP.
- Test Rigorously: Build prototypes, test edge cases, and iterate to enhance reliability and performance.
- Prioritise User Experience: Design systems that balance power with simplicity, ensuring intuitive interactions for users.
- Monitor and Refine: Track performance metrics to identify bottlenecks and optimise for efficiency.
The Future of AI with Functions, AI Agent Tools & MCP
As AI and Language Models evolve, functions, AI agent Tools and MCP will drive the next wave of innovation.
Functions will enable deeper integrations with enterprise ecosystems, AI Agent Tools will become more autonomous and contextually aware and MCP will refine context management for increasingly sophisticated interactions.
From intelligent virtual assistants to fully automated business processes, this trio is paving the way for AI that acts, adapts and delivers with precision.
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
