Beginner's Guide to Agents: A Complete Breakdown of MCP, CLI, and Skills Core Concepts

In the AI world, Agents are undoubtedly the star of the show right now. But the swarm of terminology surrounding them—MCP, CLI, ReAct, Plugin, Skills—often intimidates newcomers. This article borrows from the Feynman learning technique, using the most accessible analogies to explain Agent technology from the ground up in one go.

What Exactly Is an Agent: A Hireable Digital Employee

The defining characteristic of an Agent is its ability to understand goals based on user requirements, break down tasks, invoke tools, and autonomously complete work. Here's a vivid analogy: when you install an Agent, it's essentially like hiring a digital employee.

This is precisely why Agents were designed—users only need to propose an idea, and the Agent handles all the execution, with humans reviewing the final output. From this perspective, anyone using an Agent can transform into a "cyber entrepreneur" managing a team of "digital employees."

To truly understand how this employee works, we need to break it down into three parts: the agent's composition, how it works, and how to maximize its capabilities.

The Agent's Brain: From Probability Functions to Reasoning Models

The most core component of an agent is its brain—the LLM (Large Language Model). At its essence, a large model is a language probability distribution function—it predicts "what the next word is most likely to be."

From a technical perspective, large language models are trained on the Transformer architecture, learning to predict the probability distribution of the next token in a sequence through self-supervised learning on massive text corpora. The model internally contains billions or even trillions of parameters that encode grammar rules, world knowledge, and certain reasoning patterns. But fundamentally, it remains a statistical model—given preceding text, it outputs a probability ranking of subsequent words. This means early models, while capable of generating fluent text, were prone to "skipping steps" or producing "hallucinations" in scenarios requiring multi-step logical reasoning.

From "Confucian Pedant" to Thinker

Early large models had a classic AI joke: ask it "The gas station is a 5-minute walk away, should I drive or walk to refuel?" and the model might answer "Walk there, it saves fuel and is eco-friendly." Models at this stage were like a learned scholar who couldn't answer slightly complex questions—full of knowledge but lacking practical reasoning, unable to serve as an Agent's brain.

Chain of Thought (CoT): Installing a Prefrontal Cortex

To solve this problem, a Google team proposed the Chain of Thought (CoT) method. The core logic is remarkably simple: like solving a proof on an exam, force the model to write out its reasoning steps and prohibit jumping directly to an answer.

The Chain of Thought method was formally introduced by Jason Wei and colleagues from the Google Brain team in their 2022 paper Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. The research found that when model scale exceeds a certain threshold (approximately 100B parameters), simply adding prompts like "Let's think step by step" can significantly improve accuracy on arithmetic, commonsense reasoning, and symbolic reasoning tasks. This discovery revealed the "emergent abilities" of large models—certain capabilities don't grow linearly but suddenly appear once scale crosses a critical point.

With CoT, the model first identifies the goal (refueling), then reasons step by step: the fuel tank is in the car → the car needs to be at the gas station → the car can't drive itself there without you → conclusion: you need to drive. The emergence of this capability is equivalent to the brain developing a prefrontal cortex responsible for logical reasoning.

Reasoning Models: Multi-Path Cross-Validation

Building on CoT, designers further developed Reasoning Models. These attempt multiple paths and cross-validate—for instance, while doing forward reasoning, they simultaneously use proof by contradiction to check the "walking there" hypothesis, ultimately arriving at a consistent conclusion.

From a technical implementation standpoint, reasoning models (such as OpenAI's o1 series) incorporate reinforcement learning and Process Reward Models during training, rewarding not just correct answers but also correct intermediate reasoning steps. During inference, the model generates multiple reasoning paths (similar to Monte Carlo Tree Search), internally evaluates and cross-validates each path, and ultimately selects the conclusion with the highest consistency. This "slow thinking" mechanism sacrifices inference speed but achieves breakthrough performance on tasks requiring rigorous logic, such as math competitions, code generation, and scientific problems.

At this stage of development, the AI brain has surpassed humans in certain domains for complex problem-solving, capable of handling IMO math competitions, scientific derivations, multi-step planning, and other advanced tasks.

From Brain to Limbs: Tool Calling and Two Operating Paradigms

A thinking brain alone isn't enough—to make an Agent actually do work, it needs to operate a computer. To this end, agent designers introduced the Tool Calling mechanism: through training and interface design, models learn to identify tasks, select appropriate tools, and generate invocation parameters in specified formats.

Tool Calling implementation relies on Function Calling training. Developers pre-define tool names, descriptions, and parameter schemas (typically in JSON Schema format). During reasoning, the model determines whether the current task requires external tool intervention. If so, instead of generating a natural language answer directly, the model outputs a structured function call request. The runtime environment executes the call, returns the result to the model, and the model continues reasoning or generates a final answer based on the returned result. This design elegantly decouples the model's "thinking" capability from external systems' "execution" capability.

Two Ways Humans Use Computers

Before understanding how Agents operate computers, let's review the two main ways humans do it:

• CLI (Command Line Interface): Operating the computer by typing text commands. Like hackers in movies—no pretty desktop or icons, just a few lines of commands and the computer starts executing.
• GUI (Graphical User Interface): Windows and macOS wrap complex operations into windows, buttons, and icons—click "delete" or "copy" and the operation is done.

Two Paths for Agents to Connect with Tools: MCP and CLI

Corresponding to these two human approaches, Agents also have two mainstream methods for connecting to tools:

The MCP approach—full name Model Context Protocol—is not a specific tool but a set of standards for universal compatibility. MCP was open-sourced by Anthropic in late 2024, inspired by the USB-C concept—one standard interface connecting all devices. Before MCP, every AI application needed custom adapter code to integrate external tools, meaning N models × M tools required N×M integration solutions. MCP reduces this complexity to N+M by defining a unified client-server protocol. The protocol includes three primitives—Resources, Tools, and Prompts—and supports two transport methods: local process communication (stdio) and remote HTTP connections, enabling third-party developers to independently publish MCP Servers that any compatible client can invoke.

Under this standard, various external tools can be made into buttons on a "remote control"—this button checks email, that one sends messages, another generates video. The Agent simply clicks the appropriate function based on the task, following logic very similar to how humans use a GUI.

The CLI approach is essentially identical to how humans operate the command line—directly inputting text commands to operate the computer. The command line interface offers unique advantages for Agents: first, CLI commands are pure text, perfectly matching the native output format of large language models without requiring additional visual understanding capabilities; second, virtually all low-level OS operations can be performed via command line, with coverage far exceeding GUI; finally, CLI output is typically structured text, making it easy for models to parse execution results. In contrast, GUI-based Computer Use approaches require models to have screenshot comprehension and precise coordinate positioning abilities, which still suffer from relatively high error rates and latency.

Both approaches have their pros and cons. As Agents become increasingly intelligent, they often choose the appropriate invocation method on their own based on the task. As everyday users, you don't need to dive into the technical details.

How Agents Work: The ReAct Framework and Agent Loop

With a brain and limbs in place, a workflow is still needed. When humans execute complex tasks, they rarely plan once and execute perfectly to completion—instead, they "take it one step at a time." Many famous military campaigns exemplify this adaptive approach.

Agents follow similar logic, with the most classic being the ReAct framework: Reasoning + Acting in continuous loops until the goal is achieved.

The ReAct framework was proposed by Princeton University and the Google Brain team in their 2022 paper ReAct: Synergizing Reasoning and Acting in Language Models. The framework's core innovation lies in interleaving "thinking" and "acting" within the same generation sequence, rather than separating them into independent modules. Specifically, at each step the model first generates a Thought (analyzing the current state and planning the next step), then generates an Action (executing a specific operation), and receives an Observation (environmental feedback), forming a Thought-Action-Observation loop. This design enables models to dynamically adjust strategies based on intermediate results, effectively handling complex scenarios with incomplete information or changing environments.

This repetitive process of reasoning, acting, and observing is called the Agent Loop. In engineering practice, the Agent Loop typically sets maximum iteration counts and termination conditions to prevent infinite loops. In each cycle, the system concatenates historical dialogue, tool call results, environmental observations, and other information into context that's fed to the model. As cycles increase, the context window may fill up, requiring strategies like summary compression, sliding windows, or memory retrieval to manage long-term information. Mainstream Agent frameworks (such as LangChain, AutoGPT, CrewAI) all build different levels of abstraction around this loop mechanism.

It's precisely this loop mechanism that enables Agents to handle real-world tasks full of uncertainty, rather than mechanically executing one-time instructions.

How to Use Agents Well: Plugins and Skills

Returning to the Agent's original purpose—helping humans get work done. If you imagine an Agent as a "top-university graduate intern," you need to equip them with appropriate tools and professional skill sets to unlock their full potential.

Taking Codex as an example, its interface specifically provides two types of downloads: Plugins and Skills. The separation of Plugins and Skills reflects the philosophy of "capability as a service": Plugins focus on connecting external APIs and tool chains, solving the "can it be done" problem; Skills focus on encapsulating task patterns and best practices, solving the "how to do it well" problem. This modular architecture allows Agent capabilities to be contributed and iterated by the community like an App Store—users can quickly build specialized AI workflows by combining different Plugins and Skills without understanding the underlying implementation.

Plugins: Adding Professional Capabilities to Your Agent

Take the Remotion plugin as an example: once installed, you can tell the Agent "Please use Remotion to create financial data animation effects similar to this, with blue at 80% opacity." With it, even if the underlying model lacks image capabilities, it can produce visual effects. Plugins are like handing your intern a professional toolbox.

Skills: Enhanced Prompts

Last year's viral concept of "Prompts" helps explain Skills. A Skill is essentially an enhanced prompt: unlike a single-sentence prompt, a Skill typically exists as a folder containing skill descriptions, permission lists, rules, screenshot references, and other comprehensive information.

This way, users don't need to repeatedly describe requirements or gather materials—they just need to select appropriate plugins and optimize Skill configurations based on output results. Even plugin installation and removal can be handed off to the Agent with a single sentence, making it extremely convenient.

Summary: Understanding the Complete Agent Framework

From large model Chain of Thought reasoning, to Tool Calling with MCP/CLI paradigms, to the ReAct loop and capability extensions through Plugins and Skills—the complete picture of Agents isn't actually complicated. Once you understand this "brain—limbs—workflow—skill sets" framework, you've cleared the main obstacles to getting started with Agents. Rather than staying at the conceptual level, why not download an Agent and try it hands-on?