4-1 Why AI Agents Need MCP?

Picture a senior developer implementing a user authentication feature. She starts by checking the existing auth patterns in the codebase, writes the new endpoint, creates tests, runs them, fixes a few edge cases, and finally submits a pull request. The entire process requires jumping between VS Code, the terminal, browser, and GitHub.

Now imagine an AI agent doing this same workflow automatically. It scans your codebase, writes code that matches your team's style, creates comprehensive tests, runs them, fixes failures, and even opens a pull request. You just tell it: "Add password reset functionality" and walk away.

This level of automation is what MCP (Model Context Protocol) enables. But to understand why MCP matters, we first need to understand what AI agents actually need to work effectively.

How AI Agents Actually Work

Most developers know AI as a code completion tool or chat assistant. AI agents are different. They don't just generate code—they complete entire workflows from start to finish.

Here's a practical example. When you tell Cursor's AI agent: "Implement the getUserProfile function for our Express API," instead of just generating a function skeleton, the agent executes this complete workflow:

1. Analyzed the existing codebase to understand our API structure and authentication patterns
2. Wrote the function following our team's error handling conventions
3. Created comprehensive unit tests covering success cases, error scenarios, and edge cases
4. Ran the test suite to verify everything worked
5. Fixed a database connection issue that surfaced during testing
6. Ran the tests again until all passed

You only provide the initial goal. The AI agent handles the planning, implementation, testing, and debugging cycles that would normally take 30-45 minutes.

This workflow mirrors how experienced developers actually work. We don't just write code—we gather context, plan our approach, implement, test, and iterate. AI agents that can follow this same process are far more valuable than simple code generators.

The Three Building Blocks Every AI Agent Needs

Google's AI agent research paper breaks down what makes these systems work. Every effective AI agent needs three core components working together:

The Model: This is the reasoning engine—GPT-4, Claude, or Gemini—that understands code, plans solutions, and generates responses. When you ask for a function, the model figures out what the function should do and how to implement it.

The Orchestration Layer: This is the coordinator that manages the entire workflow. In Cursor, this layer remembers what you've done, breaks complex tasks into smaller steps, and decides what to do next. It's like having a project manager who tracks progress and keeps everything moving forward.

Tools: This is where AI agents go from helpful to transformative. Tools let agents actually do things—run commands, call APIs, interact with files, and integrate with your development environment.

Without tools, AI agents are just very smart text generators. With tools, they become development partners.

The Tool Integration Problem

Here's the reality: most AI assistants are stuck in a sandbox. They can write brilliant code, but they can't actually integrate with your development workflow. Let me show you three scenarios where this limitation really hurts.

Scenario 1: Manual Ticket Management You receive a Linear ticket: "Add rate limiting to the user registration endpoint." Instead of copying the ticket description to Cursor, you want to just tell the AI: "Work on LINEAR-1234." The agent should fetch the ticket, understand the requirements, check for existing rate limiting patterns in your codebase, and implement the feature.

Right now, you have to manually bridge this gap. You copy the ticket details, explain the context, and guide the AI through your project structure. It's inefficient and error-prone.

Scenario 2: Limited Testing Automation You've just implemented a new checkout flow. You want to test it end-to-end, but writing Playwright tests manually takes time. You'd prefer to tell the AI: "Test the checkout flow and write the automation scripts."

The AI could navigate your app, identify the user journey, write comprehensive tests, and even fix issues it discovers. But without browser automation tools, it can only generate test code that you have to run and debug yourself.

Scenario 3: Fragmented GitHub Workflow Your team uses GitHub with specific PR templates, review assignments, and merge policies. After implementing a feature, you want the AI to handle the entire PR workflow—create the branch, write the PR description, assign reviewers, and merge once approved.

Today, you have to do this manually. The AI can help write the PR description, but you handle the rest. It's a missed opportunity for automation.

Each scenario shows the same pattern: AI agents become exponentially more powerful when they can interact with your existing tools and workflows.

The Integration Nightmare

Here's the technical reality: every tool has its own API design, authentication method, and data format. Each integration requires different code, different error handling, and different maintenance.

For AI companies, building individual integrations for every tool would be impossible. Imagine if Cursor had to build and maintain separate integrations for Linear, Jira, GitHub, GitLab, Slack, Discord, Notion, and hundreds of other tools that developers use.

For developers, this fragmentation means that AI agents can only work with whatever tools their creators decided to support. You're locked into a specific ecosystem instead of using the tools that work best for your team.

This is exactly the problem that MCP solves.

MCP: The Universal Protocol for AI Tools

Model Context Protocol (MCP) is a standardized way for AI agents to communicate with external tools and services. Instead of every AI application building custom integrations with every tool, MCP provides a common language they can all speak.

Here's how it works: MCP defines a standard protocol for AI agents to send requests and receive responses from external services. Think of it like HTTP for AI tool integration—it doesn't matter what programming language or framework a tool uses, as long as it can speak MCP.

The protocol was developed by Anthropic in late 2024, but it gained industry support quickly. By early 2025, major AI companies including OpenAI and Google had adopted MCP as their standard for tool integration.

This matters because MCP creates a network effect. When a tool supports MCP, it becomes available to all AI agents that support the protocol. When an AI agent supports MCP, it can instantly connect to hundreds of tools without custom integrations.

For developers, this means freedom. You can use the AI assistant you prefer with the tools your team actually uses, without being locked into a specific ecosystem.

In the next section, we'll see how MCP works in practice and walk through connecting Cursor to the tools you use every day, transforming it from a code assistant into a true development partner.

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This article is part of 《Cursor Workflow for Engineers — Boost Development Productivity with AI Agents》. Through this series, we share practical experience from helping teams adopt AI tools and improve their development workflows. If you're interested in the full course, you can join E+ Growth Plan to access video tutorials and additional resources.