MCP Client Concepts: A Beginner Friendly Introduction

Suhas Bhairav
Aug 3
4 min read

In the evolving landscape of intelligent applications, the Model Context Protocol (MCP) offers a structured way for AI systems to access real-world context and tools through client-server communication. While servers bring capabilities like APIs, databases, and documents into play, it’s the MCP clients that make it all happen behind the scenes.

Think of an MCP client as the translator and gatekeeper between an AI application (like Claude.ai or a smart IDE) and the external data or tools provided by MCP servers. Understanding how these clients work—and the powerful features they support—is key to building secure, human-centered AI workflows.

MCP Client Concepts: A Beginner Friendly Introduction

🎯 What is an MCP Client?

MCP clients are protocol-level components created by the host application (e.g., Claude Desktop, Visual Studio Code) to manage a one-to-one connection with a single MCP server.

Host Application: The interface users interact with (e.g., IDE, chatbot).
Client: Connects the host to an MCP server, handles communication, ensures safety.

An AI application may spawn multiple MCP clients—one for each server it connects to—managing them behind the scenes while users interact with a seamless interface.

🔑 Key Features of MCP Clients

MCP clients do more than just relay requests—they enable powerful server-side features like:

Sampling – Letting servers request language model completions.
Roots – Controlling server access to local file systems.
Elicitation – Allowing servers to gather additional inputs from users dynamically.

Each of these contributes to making AI applications more context-aware, flexible, and—most importantly—secure.

🧠 Sampling – Smart AI Without Leaking Control

Sampling allows servers to ask the client to generate an AI response on their behalf using the model already integrated in the host application.

Why It Matters:

Servers don’t need direct access to AI models (saving on API costs).
Host applications stay in control of permissions and data privacy.
Human-in-the-loop checkpoints ensure no AI action goes rogue.

Sampling in Action:

Imagine a travel booking server tool called findBestFlight. It pulls 47 flight options and asks the AI to recommend the best one based on:

Morning departures
Max one layover
Lowest cost

The server sends a sampling request to the client like this:

{
  messages: [{ role: "user", content: "Analyze these flights..." }],
  modelPreferences: {
    hints: [{ name: "claude-3-5-sonnet" }],
    intelligencePriority: 0.9
  },
  systemPrompt: "You are a travel expert...",
  maxTokens: 1500
}

The client shows this request to the user for approval. Only after user consent does it proceed, ensuring full transparency.

🛡️ Sampling = Secure AI Delegation

Sampling is built on strong human-in-the-loop design principles:

Explicit Approvals: Users approve or edit the request and its result.
Transparency: Prompts, models, and context are clearly shown.
Custom Controls: Users can configure trust levels and redactions.
Isolation: Servers can’t access prior conversations unless explicitly allowed.

This makes sampling perfect for agentic use cases like summarization, complex reasoning, or trade-off analysis—without compromising control.

📁 Roots – File System Boundaries That Respect Privacy

Roots define what parts of the file system an MCP server is allowed to access.

Why Roots Matter:

Rather than giving servers full disk access, the client can tell them:

“You can only operate inside this folder.”

This limits data exposure and keeps AI interactions scoped and safe.

Example:

A travel planner app shares the following roots:

[
  { "uri": "file:///Users/agent/travel-planning", "name": "Travel Workspace" },
  { "uri": "file:///Users/agent/client-documents", "name": "Client Docs" }
]

The travel server can:

Read templates for itineraries
Access saved passports and tickets
Write new trip plans

But it cannot access other folders, like financial records or unrelated projects.

Roots can also change dynamically as the user opens or closes folders. The server is notified with roots/list_changed.

🧩 Elicitation – Real-Time User Input for Dynamic Tasks

Elicitation allows servers to ask for more information mid-task, instead of failing or guessing.

It’s especially powerful in complex workflows where all information isn't known up front.

How It Works:

Server sends a elicitation/requestInput request.
Client shows a structured UI to the user.
User reviews, fills out, and submits.
Server resumes its operation with updated input.

Example: Finalizing a Vacation Booking

Before finalizing a trip, the server might need user confirmation:

{
  message: "Please confirm your Barcelona vacation details",
  schema: {
    type: "object",
    properties: {
      confirmBooking: { type: "boolean" },
      seatPreference: { type: "string", enum: ["window", "aisle"] },
      travelInsurance: { type: "boolean" }
    }
  }
}

Users provide this input in real time—through dropdowns, checkboxes, and text fields.

👥 Human-Centered Design: Every Step Reviewed

Across sampling, roots, and elicitation, the MCP client ensures:

User control – Every action is user-approved.
Clear UI – Users understand what’s being asked and why.
Flexible security – Advanced users can fine-tune access.
Privacy-first – No passwords, keys, or sensitive data shared without consent.

🌐 Putting It All Together: A Client in Action

Here’s how a single MCP client might operate in a travel app:

Connects to a travel server
Shares roots for itineraries and templates
Elicits user preferences for trip booking
Requests sampling to evaluate best flight options
Returns AI-generated recommendations after user review

Everything happens under the hood—while users see a clean, intelligent interface that responds with rich functionality and full transparency.

✨ Final Thoughts

MCP clients are the unsung heroes of secure, interactive AI systems. They bridge the gap between human users, intelligent models, and external data—with trust, security, and flexibility built in.

If you’re building AI-powered software, understanding and leveraging client features like sampling, roots, and elicitation can take your user experience from basic to exceptional.

Stay tuned with Metric Coders for more hands-on breakdowns of the tools powering tomorrow’s intelligent apps.