July 21, 2025

How MCP, Agent, RAG, and Function Call Work Together？

consulting@sinokap.com

https://it-support-china.com/

Large Language Models (LLMs) are all the rage today — they can write poems, chat, code, and seem capable of almost anything. But you may have noticed: sometimes they’re a bit “naively smart.” Ask for the weather, and it says “Based on my knowledge base…” — but can’t tell you today’s temperature. Ask it to organize local files, and it looks helpless.

This all stems from a fundamental “limitation” of LLMs: they’re like brilliant brains cut off from the outside world — knowledgeable, but unable to see or act. To truly make LLMs practical and reliable, the tech community has introduced a set of powerful tools: Agent, RAG (Retrieval-Augmented Generation), Function Call, and a rising star — MCP (Model Context Protocol).

Today, Sinokap will break down these four core concepts using plain language and vivid analogies — so you’ll understand how they work together to turn LLMs from “theoretical wizards” into “hands-on doers.”

Comparison of Core Frameworks

1. Agent:

A goal-driven project manager — the “brain” of the operation.

2. RAG and Function Call

1-RAG is responsible for searching for information and finding evidence;

2-Function Call is responsible for executing specific operations and calling external APIs.

3. MCP

Committed to providing a standardized interface specification, so that Agent can access and use various tools more conveniently and uniformly (whether RAG functions or other tools implemented by Function Call).

Analysis of AI Core Capabilities

1. RAG: Give LLM a "data collection list"

RAG (Retrieval-Augmented Generation) is a technical framework that makes AI answers more reliable. Simply put, before AI answers questions, it first searches for (retrieval) relevant information in a designated database (such as internal company documents, the latest industry reports), and the answer is based on the latest, accurate, and specific facts to avoid "confident lying."

2. Function Call

Function Call is a key feature of the Large Language Model (LLM), which allows the model to "request" external programs to assist in completing tasks in specific scenarios. Note that this is "request" rather than "personally executed", because LLM itself cannot actively access the network, query real-time data, or call the operating system.

With Function Call, LLM can issue call instructions like a commander to allow external tools to complete actions such as query, processing, or operation. For example, when you say "check the weather in Beijing today" to the smart speaker, the speaker itself does not have the ability to perceive the weather, but it will trigger a weather query application (i.e. a predefined function) to obtain data such as "sunny, 25 degrees", and then LLM will translate it into natural language to reply to you.

3. Agent: LLM becomes a universal executive

Agent is a more advanced and autonomous AI system. It uses LLM as its core "brain". It can not only understand your goals, but also think and plan steps by itself, and actively call tools (such as RAG and Function Call mentioned above) to perform tasks and interact with the external environment.

As a system, it can autonomously plan tasks → retrieve information → call functions → generate results.

Example:

Request: "Help me plan a business trip to Shanghai next week, book hotels and flights, and organize the itinerary." The Agent will complete the following operations by itself:

1-Plan subtasks

2-Use RAG to check company policies

3-Use Function Call to adjust flight and hotel APIs

4-Summarize the itinerary and return

4. MCP

MCP (Model Context Protocol) is a standard communication protocol proposed and open-sourced by Anthropic at the end of 2024. It aims to provide a unified interface specification for the interaction between AI applications (as clients) and external data sources or tools (as servers), enabling models to access and call external capabilities in a standardized way and simplify the integration process.

You can think of MCP as a "universal adapter" for AI to connect to external tools. Whether it is a local file, database, or online platforms such as Slack and GitHub, as long as they follow the MCP protocol, AI can deal with them directly without having to "relearn" every time.

Advantages:

1-Supports dynamic discovery tools

2-Easier to expand without changing the Agent end

3-Provides call permissions and security control

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