I will show you how to reduce AI hallucination and get the best result from your agent

This content originally appeared on DEV Community and was authored by Md. Al-Amin

Hello fellow devs,

Today I want to talk about something many of you are already familiar with, but this post is mainly for those who are not. You might already be guessing that I’m talking about MCP (Model Context Protocol)—and you’re right.

Now, some of you may say, “Everyone knows about MCP already,” and I agree. But there are still many people who don’t really know about its capabilities, and this post is for them.

I’m also planning to start a series of posts exploring MCP servers and will be sharing them here.

What is MCP?

MCP stands for Model Context Protocol. It’s a standard way to connect external tools, APIs, and data sources directly to your AI agent. Instead of relying only on what the model “remembers” or “guesses,” MCP gives the agent structured context and reliable information.

A simpler way to explain: suppose you want your agent to access your website, database, or updated documentation—MCP makes that possible.

Why do we need it?

Most AI models are limited by their training data. When we ask something outside that scope, they often hallucinate—producing answers that sound right but are actually wrong. MCP solves this by letting the model fetch real, up-to-date, and accurate information instead of inventing it.

Sometimes we waste requests by just giving prompt after prompt, but the agent still doesn’t understand the task. With MCP, the agent can access the output directly, identify problems, and fix them without constant intervention.

Today I’ll talk about 3 MCP servers that you can use in your daily coding life:

Sequential Thinking → Breaks down complex tasks into smaller parts, making them easier to solve while reducing the risk of hallucinations.
Memory → Gives your agent memory across sessions/chats.
Context7 → AI models are trained on old data, but technology evolves rapidly. With Context7, your agent can stay updated with the latest tools and documentation.

How to Install MCP Servers in VS Code

Here’s how you can set up these powerful tools in your development environment.

Method 1: VS Code Extension (Recommended)

Open VS Code and go to the Extensions view (Ctrl+Shift+X).
Search for “MCP” in the extensions marketplace.
Look for the MCP section—you’ll see various MCP servers available.
Click on the MCP section—this will redirect you to a web page where you can browse and install different MCP servers.
Install your chosen servers—you can install multiple servers to enhance your AI agent’s capabilities.

Method 2: Manual Installation with `mcp.json`

If you prefer more control over your MCP configuration, you can set it up manually:

Create an mcp.json file in your project root:

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-sequential-thinking"]
    },
    "memory": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-memory"]
    },
    "context7": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-context7"]
    }
  }
}

Install the servers using npm:

npm install @modelcontextprotocol/server-sequential-thinking
npm install @modelcontextprotocol/server-memory
npm install @modelcontextprotocol/server-context7

Method 3: Global Installation

For system-wide access to MCP servers:

npm install -g @modelcontextprotocol/server-sequential-thinking
npm install -g @modelcontextprotocol/server-memory
npm install -g @modelcontextprotocol/server-context7

Real-World Examples

Here’s how MCP servers can transform your AI interactions:

Before MCP (Typical AI Response):

User: "Help me optimize this React component for performance"

AI: "You can use React.memo and useMemo. Here's a basic example..."

After Sequential Thinking MCP:

User: "Help me optimize this React component for performance"

AI: "Let me analyze this step by step:

1. First, I'll examine your component structure
2. Identify potential performance bottlenecks
3. Consider different optimization strategies
4. Provide specific recommendations with reasoning

Based on my analysis, here are the key areas for optimization..."

Before MCP (Memory Issues):

User: "Remember that React component we worked on yesterday?"

AI: "I don't have access to our previous conversation..."

After Memory MCP:

User: "Remember that React component we worked on yesterday?"

AI: "Yes! We were working on the UserProfile component that had performance issues.  
I remember we implemented React.memo and useCallback. Let me continue from where we left off..."

Since I started using these MCP servers, my development experience has improved dramatically:

Faster problem-solving with systematic thinking
Consistent code quality through remembered preferences
Up-to-date solutions with current documentation access
Reduced debugging time thanks to better context awareness

If this post gets good feedback, I’ll keep sharing more in this MCP series.