A new term has been gaining traction in AI discussions: agentic AI tools. These tools allow AI agents — such as those powered by large language models (LLMs) — to interact with the real world. While LLMs are remarkably skilled at generating text and responding to complex prompts, they operate within the confines of their training data. They don’t have built-in access to live information or the ability to take real-world actions. They can’t check the weather, send a message, or update a database.

This is where Model Context Protocol (MCP) has started drawing attention. MCP aims to provide a structured way for AI systems to connect with external tools, bridging the gap between language models and real-world functionality. Its role will be discussed later, but first, it is important to understand what an agentic tool actually is.

Why Do AI Agents Need Tools?

At first glance, LLMs seem impressively capable. They can draft emails, summarize reports, and even engage in deep philosophical discussions. But outside of pure text generation, they quickly hit a wall. They don’t know what time it is. They can’t send an alert. They can’t update a to-do list or place an order.

Some might wonder — why not just train the model with all the information it needs? The problem is that knowledge embedded in training is static. Even if a model were trained to always know the current time, it would be outdated a zeptosecond later. The same applies to any dynamic information — prices change, appointments get rescheduled, messages need to be sent. No amount of training can make an AI model inherently aware of, or capable of acting on, real-time events.

The ability to retrieve live data, such as checking the latest stock prices, and take action, such as sending a notification, happens outside the training process. Agentic tools provide this missing link, allowing AI agents to interface with the world in real-time rather than just reasoning about it in isolation.

For an AI agent to perform a certain task, the necessary agentic tools must exist first. If an AI assistant needs to book a meeting, there must be a tool that can interact with a calendar system. If it needs to process a payment, there must be a tool capable of securely handling transactions. Before an agent can act, the right tools must be developed and made available to it.

With this foundation in place, the next step is understanding how an agent determines when and how to use these tools.

Explicit vs. Inferred Invocation

The key to understanding agentic tools is recognizing how they are invoked. Traditional software systems rely on explicit invocation, while AI-powered agents rely on inferred invocation.

Explicit Invocation (Direct and Controlled)

‍In most software, a developer explicitly calls a procedure with a specific name and precise parameters. The procedure always does the same job in a predictable way. The computer doesn’t decide whether or not to use it; the developer makes that decision ahead of time.

Inferred Invocation (AI Decides When to Act)

‍An AI agent, on the other hand, infers when it should use a tool based on the user’s request. It reads your natural-language input (“What’s Bob’s phone number?”), recognizes that retrieving contact info is required, and infers that the “find contact info” tool is the right one to use. No human developer has hand-coded that choice at the moment of your request — the AI makes the decision on its own.

Inferred invocation is what makes agentic tools powerful, but it also introduces new challenges that don’t exist in traditional software.

Challenges of Inferred Invocation

Inferred invocation allows AI agents to decide autonomously when and how to use tools, making them flexible and adaptive. However, this flexibility introduces significant challenges that traditional, explicitly invoked software does not face. The most fundamental issue is that existing procedures cannot simply be turned into tools. Procedures are designed for structured code execution, while tools must be designed for AI agents that interpret natural language, infer intent, and make autonomous decisions. This difference means that an additional layer must be built — one that reshapes traditional functionality into something AI can effectively understand and invoke. Without this, the entire process of inferred invocation would break down.

These challenges primarily involve tool design, language interpretation, and scalability, all of which must be carefully managed to ensure AI agents make the right decisions.

Tools vs. Traditional Procedures: Why Documentation Matters More

‍In traditional software, procedures are designed for code, not humans. A procedure can have dozens of parameters with complex structures, and as long as developers call it correctly, the system remains stable. Even if the procedure has no documentation, the software will still execute it as expected.

Moreover, while traditional procedures can have many parameters without issue, agentic tools must be designed for simplicity. If a tool has too many parameters or overly complex inputs, the AI may struggle to infer the correct way to use it. Unlike explicit procedure calls, where parameters are manually defined in code, AI agents rely on reasoning to supply the right inputs. The more parameters a tool has, the higher the chance of confusion.

Bridging the Naming Gap

‍Another major challenge is the disconnect between how tools are named and how users describe their needs. Traditional software procedures have precise, predefined names, and developers explicitly call them when needed. AI agents, however, must interpret human language and match it to the appropriate tool.

For example, a backend system might have a procedure called getUpcomingEvents(), but users will phrase their requests differently — “What’s on my schedule?”, “Do I have any meetings today?”, or “Show my next appointment.” Unlike conventional software, where developers map specific inputs to predefined procedures, AI agents must infer the connection between human intent and system logic.

This mismatch extends beyond simple phrasing differences. Backend systems often use technical or generic names that don’t align with user expectations. A hotel booking system might refer to “reserving a room” as “resource allocation,” or a payment system might process a refund under “reverse transaction” rather than simply “issue refund.” If an agentic tool is not named intuitively, AI agents may struggle to use it correctly.

To bridge this gap, tool design must prioritize clear, user-friendly naming that aligns with how people naturally phrase requests. Otherwise, AI agents may misinterpret the available tools, leading to errors or inefficient responses.

Scalability: Why More Tools Can Lead to Worse Performance

‍In traditional software, scalability is straightforward — new procedures can be added indefinitely without disrupting existing functionality. Since every procedure is explicitly called when needed, adding more does not create ambiguity.

With agentic tools, however, scalability is much more delicate. AI agents must infer which tool to use based on user input, meaning the more tools they have access to, the harder it becomes to make the correct choice. If multiple tools have overlapping capabilities, the AI may pick the wrong one or hesitate between options.

For example, if an AI agent has both a “find contact info” tool and a “retrieve customer details” tool, it might struggle to determine which one to use when asked, “What’s Bob’s phone number?” Unlike traditional systems, where each procedure is explicitly invoked, inferred invocation introduces decision complexity as the toolset grows.

To prevent this, agentic toolsets must be carefully curated and structured. Rather than adding tools indiscriminately, developers must prioritize clarity, minimize redundancy, and consolidate similar functions. For example, instead of separate tools for “add item to cart,” “remove item from cart,” and “check cart total,” a single “manage shopping cart” tool could handle all three actions dynamically.

The NxM Problem (and How MCP Solves It)

As AI becomes more widespread, we’re seeing an explosion of different agents (chatbots, virtual assistants, recommendation engines) and a whole variety of tools (calendars, databases, analytics apps). Without a standard, each agent would need a custom way to talk to each tool — leading to a tangled web of integrations. If you have N agents and M tools, you end up with N x M unique connections to build and maintain.

MCP provides a common language and structure so that any agent can use any tool following the same rules. This drastically reduces that messy NxM problem into something far simpler. Instead of building a separate connector from every agent to every tool, you build just one connector to MCP on either side. Agents know how to call any MCP-compliant tool, and tools know how to respond to any MCP-compliant agent.

Pulling It All Together

Agentic tools are a game-changer because they let AI systems act on the world around them, rather than being locked up in their own training data. The AI relies on inferred invocation — figuring out which tool to use based on what the user wants — while traditional software is built on explicit invocation, with a developer deciding every step in advance.

By structuring these tools well, AI can interact with real-world applications smoothly. While inferred invocation introduces complexity, it also unlocks AI that can act rather than just respond — creating new opportunities for businesses and users alike.