I understand! On the role and difference between MCP Server and workflow in intelligent agent development scenarios

Today we are discussing a very boring topic - meaning, but it has troubled me for a long time. Recently, I suddenly had an epiphany, and I feel like I have opened my eyes. Now I would like to share with you some of my recent thoughts on workflow and Mcpserver in intelligent agent development, and hope to get your corrections.

Before reading this article, it is assumed that you already have a certain understanding of intelligent agent development, Mcp workflow, and dify. If you haven’t learned about them yet, you can read my previous articles.

Three types of agent representations: chat assistant, workflow, and dialogue flow

No more confusion! This article reveals the core differences between MCP Server, Function Call and Agent

In fact, whether it is workflow or MCP Server, there are mainly two intelligent agent routes suitable for traditional industries:

1. Big model + MCP + software tools, this is more down-to-earth and has more production value, because the production and processing of professional data and information is still taught to traditional professional production software to operate, but the decision maker has changed from a person to a big model. In this route, the big model plays the role of military strategist and general, making plans, deploying troops and leading troops to fight.

2. Big model + RAG + knowledge base/workflow. This route is currently being researched by many people and appeared relatively early, such as the Coze and Dify platforms. However, there are only a handful of truly valuable implementation scenarios. Friends who have played with workflows should know that in this route, the big model plays the role of a big soldier, doing all the small, dirty and tiring work .

The main difference between the above two routes is the question of which one is dominant, the MCP mode or the workflow.

Dify-type workflows are a kind of artificially set thinking and action chains. When to think and when to use what tools are set by people, based on their professional abilities. The MCP model transfers the right of thinking and action to the big model, allowing the big model to decide what to think about next, what tools to use, which interfaces to call, and what data to obtain through the ReAct paradigm. 

However, if you want to do the MCP model well and get stable and reliable output results every time , you must use structured prompts or workflows to guide large models to use MCP. The prompt design and workflow design based on MCP will also be a higher-level programming language.

In the end, you are in me and I am in you. Dify workflow can use MCP Server as a key node in the workflow; similarly, Dify workflow can be published as Mcp Sever, which can be selected and used by the big model.

MCP Server and Dify workflow reconstruct the intelligent agent development paradigm from the protocol layer and application layer respectively: the former is like the "USB HUB of the intelligent agent", solving the problem of tool access standardization; the latter is the "flowchart drawing board of the intelligent agent", solving the problem of task execution structure. The coordinated use of the two can not only break through the limitations of the tool ecosystem through MCP, but also ensure the controllability of the core business process with the help of Dify, and jointly promote the evolution of AI applications from "function stacking" to "intelligent collaboration".

In short, MCP solves the problem of tool fragmentation, and Dify solves the problem of process fragmentation. Together, they build a complete Agent development ecosystem, and the two have complementary capabilities.

However, as platforms for connecting large models and MCP, Cursor, Claude Desktop, Vscode + Cline, and various paid or free large model Chat clients have recently launched functions. In fact, software in general traditional industries requires programming, so Cursor + MCP is more practical, and Chat client + MCP is currently more suitable for personalized small scene needs.

However, a single MCP server that operates file lists, controls browsers, and reads databases seems to be a very common technology. It is just the previous script API or RPA technology encapsulated into an MCP server using the MCP protocol. The atomic function of MCP is not very meaningful. What makes sense is how to stitch these capabilities into a business project that can solve practical problems .

Manus, which became popular in early March, stitched these capabilities together, but it still focused on scenarios such as collecting data and writing useless data reports.

The core is still business needs or our human capabilities. What do we want to use it for and what problems do we want to solve? This problem cannot be solved by relying solely on technology or programmers.

Therefore, the intelligent agent scenario must be vertical, not universal. The person who can really think of the landing business scenario may be a project manager, front-line sales, company boss, or industry expert. However, these people have limited energy and cannot deeply understand the technical details. They may not be able to think of very specific scenario requirements based on MCP. The main problem is that it is difficult for practitioners in the two parts of technical solutions and business adaptation to have both capabilities at the same time.

Discovering workflows that are truly valuable to users will be the competitiveness and barrier of this industry. Technology, on the other hand, has no barriers to equal access.

The above is my recent thinking. I share it with you and hope to get your positive comments and feedback.