MCP Client: What It Is, How to Choose One, and Getting Started

By Chester Beard • December 10, 2025

#mcp#client#guide#tutorial#claude-desktop#cursor#cline

MCP Client: What It Is, How to Choose One, and Getting Started

Your AI assistant is brilliant at conversation and reasoning. But without access to your files, databases, APIs, and development tools, it’s working with one hand tied behind its back.

That’s where MCP clients come in. An MCP client is the bridge that connects AI applications to external data sources and tools through the Model Context Protocol standard. Think of it as the USB port for your AI—a universal interface that lets your assistant plug into practically anything.

If you’ve been wondering how Claude Desktop reads your local files, how Cursor understands your codebase, or how developers are building AI agents that actually do things in the real world, MCP clients are the answer.

Quick Answer: Which MCP Client Should You Use?

Before diving deep, here’s the fast track. Pick your situation:

If you are…	Use this client	Why it wins
Claude power user	Claude Desktop	Native MCP support, zero friction setup
Developer in VS Code	Cline or Roo Code	Full MCP support, open-source, excellent tool integration
Cursor user	Cursor	Built-in MCP with AI coding features
Non-technical user	Jenova	One-click server installation, mobile-friendly
Building custom apps	LangChain MCP Adapters	Framework flexibility, works with any LLM
CLI/automation fan	Claude Code or Goose	Terminal-native, no JSON editing required
Self-hosting enthusiast	LibreChat	Full control, supports local LLMs
JetBrains user	JetBrains AI MCP	Native IDE integration for IntelliJ, PyCharm, WebStorm

Still not sure? Read on—we’ll break down exactly what MCP clients do and how to pick the right one.

What Is an MCP Client?

An MCP client is a software component that implements the client side of the Model Context Protocol. In plain terms: it’s the piece of software that requests data and actions from MCP servers on behalf of an AI application.

Here’s the key distinction that trips up most people:

MCP Host: The AI application you interact with (Claude Desktop, Cursor, your custom chatbot)
MCP Client: The protocol handler inside that host that manages server connections
MCP Server: External services providing tools, resources, and data

When you ask Claude Desktop to “read my project files,” here’s what happens:

Claude Desktop (the host) receives your request
The MCP client inside Claude Desktop sends a JSON-RPC message to the filesystem MCP server
The server reads your files and returns the content
The client passes this back to Claude for processing

Most users never see the client directly—it works behind the scenes. But understanding it helps you choose the right AI application and troubleshoot connection issues.

MCP Architecture: Where Clients Fit

The Model Context Protocol follows a client-server architecture with three layers:

┌─────────────────────────────────────────────────────┐
│                    MCP HOST                         │
│  (Claude Desktop, Cursor, Your Custom App)         │
│                                                     │
│   ┌─────────────┐  ┌─────────────┐  ┌───────────┐  │
│   │ MCP Client  │  │ MCP Client  │  │MCP Client │  │
│   │     #1      │  │     #2      │  │    #3     │  │
│   └──────┬──────┘  └──────┬──────┘  └─────┬─────┘  │
└──────────┼────────────────┼───────────────┼────────┘
           │                │               │
           ▼                ▼               ▼
    ┌──────────────┐ ┌──────────────┐ ┌──────────────┐
    │  Filesystem  │ │    GitHub    │ │   Database   │
    │  MCP Server  │ │  MCP Server  │ │  MCP Server  │
    └──────────────┘ └──────────────┘ └──────────────┘

A single MCP host can run multiple clients, each connecting to a different server. This is how Claude Desktop can simultaneously access your files, query your database, and interact with GitHub—each through a separate MCP client-server connection.

What Makes MCP Clients Different from Direct API Calls?

You might wonder: why not just have the AI call APIs directly?

MCP clients solve several problems:

Standardization: Instead of custom integrations for every service, MCP provides one protocol that works everywhere
Security: Clients handle authentication, permission scoping, and data exposure in a controlled way
Discovery: Clients can query servers to learn what tools are available at runtime
Lifecycle management: Clients handle connection setup, error recovery, and graceful shutdown

Without MCP, every AI application would need custom code for every external service. With MCP, you configure a server once and any compatible client can use it.

How MCP Clients Work

MCP clients communicate using JSON-RPC 2.0 over one of several transport mechanisms:

Transport Methods

STDIO (Standard Input/Output): The most common method for local servers. The client spawns the server as a subprocess and communicates through stdin/stdout pipes. Fast, simple, and works offline.
SSE (Server-Sent Events): Used for remote servers. The client connects via HTTP and receives a stream of events. Good for cloud-hosted MCP servers.
Streamable HTTP: A newer transport that combines the flexibility of HTTP with streaming capabilities. Gaining adoption for production deployments.

Connection Lifecycle

Every MCP client follows this sequence:

Initialize: Client sends capabilities and protocol version
Negotiate: Server responds with its capabilities
Discover: Client requests available tools, resources, and prompts
Execute: Client sends requests; server responds with results
Terminate: Clean shutdown when the host application closes

Here’s what an initialization handshake looks like:

// Client → Server
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "initialize",
  "params": {
    "protocolVersion": "2024-11-05",
    "capabilities": {
      "roots": { "listChanged": true }
    },
    "clientInfo": {
      "name": "claude-desktop",
      "version": "1.0.0"
    }
  }
}

// Server → Client
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "protocolVersion": "2024-11-05",
    "capabilities": {
      "tools": {}
    },
    "serverInfo": {
      "name": "filesystem-server",
      "version": "0.1.0"
    }
  }
}

After initialization, the client can call tools:

// Client → Server
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "read_file",
    "arguments": {
      "path": "/Users/dev/project/README.md"
    }
  }
}

Understanding this protocol helps when debugging—if your MCP server isn’t responding, you can check logs for failed handshakes or malformed requests.

Types of MCP Clients

MCP clients come in several flavors, each optimized for different workflows:

IDE-Integrated Clients

These live inside your code editor, giving AI direct access to your development environment.

Cursor: The AI-first code editor with built-in MCP support. Connects to servers through its settings panel. Popular for its seamless integration of AI coding assistance with MCP tool access.
Cline: Open-source VS Code extension that turns Claude into a full coding agent. Excellent MCP support with the ability to use tools, read files, and execute commands. One of the most capable open-source options.
Roo Code: Fork of Cline with additional features and customization options. Strong MCP implementation with active development.
Continue: VS Code and JetBrains extension supporting multiple LLMs. Provides MCP integration for tool access during coding sessions.
JetBrains AI: Native MCP support in IntelliJ IDEA, PyCharm, WebStorm, and other JetBrains IDEs. Integrates with the IDE’s built-in AI assistant.

Desktop Applications

Standalone apps that provide MCP-enabled AI chat outside the browser.

Claude Desktop: Anthropic’s official desktop client with native MCP support. The reference implementation—if something works in Claude Desktop, it should work everywhere.
Goose Desktop: AI coding agent from Block (formerly Square) with guided MCP setup. Eliminates manual JSON configuration.
FlowDown: macOS-native MCP client with clean interface and strong privacy focus.
DeepChat: Cross-platform desktop app supporting multiple AI providers with MCP integration.

CLI and Terminal Clients

For developers who live in the terminal.

Claude Code: Anthropic’s official CLI for agentic coding. Full MCP support with ability to spawn and manage servers from the command line.
Goose CLI: Terminal version of Goose with interactive setup wizards for MCP servers.
5ire: Lightweight CLI client focused on developer productivity.
mcp-cli: Reference implementation for testing and debugging MCP servers.

Web-Based and Browser Clients

Access MCP-enabled AI through your browser.

Jenova: Remarkably user-friendly—one-click server installation without touching JSON config files. Mobile-responsive and great for non-technical users.
LibreChat: Self-hosted chat interface supporting multiple LLMs with MCP integration. Full control over your data.
Msty: Web-based client with MCP support and team collaboration features.

Framework and SDK Clients

For developers building custom AI applications.

LangChain MCP Adapters: Python library that exposes MCP servers as LangChain tools. Works with any LLM that LangChain supports.
Spring AI MCP: Java integration for Spring Boot applications.
Genkit MCP: Google’s AI framework with MCP client support.
OpenAI Agents SDK: OpenAI’s framework includes MCP adapter for tool integration.
Mastra: TypeScript framework for building AI agents with MCP support.

Best MCP Clients Compared

Here’s how the major clients stack up across key criteria:

Client	Transport Support	Multi-Server	LLM Support	Platform	Setup Difficulty	Best For
Claude Desktop	STDIO	Yes	Claude only	Mac, Windows	Easy	Claude users, reference testing
Cursor	STDIO	Yes	Multiple	Mac, Windows, Linux	Easy	AI-assisted coding
Cline	STDIO	Yes	Multiple	VS Code	Medium	Open-source dev workflows
Roo Code	STDIO	Yes	Multiple	VS Code	Medium	Customizable coding agents
Goose	STDIO	Yes	Multiple	CLI, Desktop	Easy	Guided setup, automation
Jenova	STDIO, SSE	Yes	Multiple	Web	Very Easy	Non-technical users
LibreChat	STDIO	Yes	Any (local included)	Self-hosted	Medium	Privacy, self-hosting
LangChain MCP	STDIO, SSE	Yes	Any	Python apps	Medium	Custom applications
Claude Code	STDIO	Yes	Claude	CLI	Easy	Terminal-based coding
JetBrains AI	STDIO	Yes	Multiple	JetBrains IDEs	Easy	JetBrains users

Feature Highlights

Best for beginners: Jenova eliminates configuration complexity. You browse available servers, click install, and they work. No JSON editing required.
Best for developers: Cline and Roo Code offer the deepest VS Code integration with full file access, terminal execution, and browser automation through MCP.
Best for privacy: LibreChat runs entirely on your infrastructure. Pair it with local LLMs for zero data leaving your network.
Best for building apps: LangChain MCP Adapters let you expose any MCP server as LangChain tools, then use them with GPT-4, Claude, Llama, or any supported model.
Best for Claude users: Claude Desktop provides the smoothest experience with guaranteed compatibility for all MCP features.

How to Choose the Right MCP Client

Use this decision framework to narrow down your options:

By Primary Use Case

I want AI help while coding → Use your IDE’s native option: Cursor, Cline/Roo (VS Code), JetBrains AI, or Continue
I want a general-purpose AI assistant with tool access → Claude Desktop for Claude users, Goose Desktop for multi-LLM flexibility
I want to build an AI application → LangChain MCP (Python), Spring AI MCP (Java), Mastra (TypeScript)
I want the simplest possible setup → Jenova for web-based, Goose for CLI (guided setup wizards)
I need to self-host everything → LibreChat with local LLM backend

By Technical Comfort Level

Non-technical: Start with Jenova. Zero configuration, visual server installation.
Comfortable with GUIs: Claude Desktop or Goose Desktop. JSON config required but well-documented.
Developer: Cline, Cursor, or Claude Code. Full control over configuration and debugging.
Building production systems: LangChain/Spring AI/Mastra SDKs for programmatic control.

Checklist: Questions to Ask

Before committing to a client, verify:

✅ Does it support STDIO transport? (required for most MCP servers)
✅ Can it connect to multiple servers simultaneously?
✅ Does it work with your preferred LLM?
✅ Is it available on your operating system?
✅ Does it support SSE/HTTP transport? (needed for remote servers)
✅ What’s the debugging experience like?
✅ Is it actively maintained?

Getting Started: Your First MCP Client Setup

Let’s get you running with Claude Desktop—the reference implementation that’s straightforward to set up and works reliably.

Step 1: Install Claude Desktop

Download Claude Desktop from claude.ai/download. Available for macOS and Windows.

Step 2: Locate Your Configuration File

The MCP configuration lives in a JSON file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

If the file doesn’t exist, create it.

Step 3: Add Your First MCP Server

Let’s add the official filesystem server so Claude can read files on your computer.

First, ensure you have Node.js installed (version 18+).

Then add this configuration:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/yourname/Documents"
      ]
    }
  }
}

Replace /Users/yourname/Documents with the directory you want Claude to access.

Step 4: Restart Claude Desktop

Quit Claude Desktop completely (check your system tray/menu bar) and relaunch it.

Step 5: Verify the Connection

In Claude Desktop, look for the MCP icon (hammer/tools) in the input area. Click it to see connected servers.

Test by asking: “What files are in my Documents folder?”

If Claude lists your files, congratulations—you’ve got MCP working.

Troubleshooting Common Issues

Server not appearing: Check that Node.js is installed and in your PATH. Try running the npx command manually in terminal.
Permission errors: Ensure the directory path exists and Claude has read access.
JSON syntax errors: Validate your config file at jsonlint.com. Missing commas and brackets are common culprits.
Server crashes on startup: Check Claude Desktop’s logs at:
- macOS: ~/Library/Logs/Claude/
- Windows: %APPDATA%\Claude\logs\

MCP Client Security Considerations

MCP gives AI significant power over your systems. Here’s how clients handle security:

Authentication and Authorization

OAuth 2.1 is the standard for remote MCP servers. Clients handle the authentication flow and store tokens securely. For local servers (STDIO transport), security relies on filesystem permissions—the server can only access what the user running it can access.

Permission Scoping

Good MCP clients let you limit what servers can do:

Read-only access: Servers can fetch data but not modify anything
Directory restrictions: Limit file access to specific paths
Tool allowlists: Enable only specific tools from a server

Claude Desktop’s filesystem server, for example, only accesses directories you explicitly configure.

Data Exposure Risks

Be aware that MCP servers can access sensitive data. Before connecting a server:

Review what data it can access
Check who maintains the server (official Anthropic servers vs. community)
Understand what data might be sent to the LLM

Enterprise Deployment

For organizational use, consider:

Running MCP servers on isolated infrastructure
Implementing network-level access controls
Using audit logging to track tool usage
Establishing approval processes for new server connections

Building a Custom MCP Client

Need to embed MCP in your own application? Official SDKs are available:

Python

pip install mcp

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async with stdio_client(StdioServerParameters(
    command="npx",
    args=["-y", "@modelcontextprotocol/server-filesystem", "/tmp"]
)) as (read, write):
    async with ClientSession(read, write) as session:
        await session.initialize()
        tools = await session.list_tools()

TypeScript

npm install @modelcontextprotocol/sdk

import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";

const transport = new StdioClientTransport({
  command: "npx",
  args: ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"]
});
const client = new Client({ name: "my-client", version: "1.0.0" });
await client.connect(transport);

Other languages: Java, C#, Kotlin, and Swift SDKs are available. Check the official MCP SDK documentation for details.

When to build custom:

You need deep integration with an existing application
Commercial clients don’t support your LLM provider
You require custom security, logging, or compliance features
You’re building a product that uses MCP internally

When to use existing clients:

You’re a user, not a developer
Existing clients meet your needs
You want community support and regular updates

Frequently Asked Questions

Is an MCP client the same as an AI agent?

No. An MCP client is a component inside an AI agent that handles communication with MCP servers. The agent is the larger system that decides when to use tools, how to interpret results, and what actions to take. The client just handles the protocol mechanics.

What’s the difference between an MCP client and an LLM?

The LLM is the AI brain that understands language and generates responses. The MCP client is the communication layer that lets that brain reach external tools and data. Without an MCP client, the LLM can only work with information in its training data and your conversation.

Can I use MCP with ChatGPT or other non-Claude models?

Yes, but not through ChatGPT’s web interface. MCP is model-agnostic—you can use it with any LLM through compatible clients. LangChain MCP Adapters, LibreChat, and other clients support GPT-4, Llama, Mistral, and other models.

Do I need an MCP client to use MCP servers?

Yes. MCP servers don’t run standalone—they wait for client connections. You need some client (whether Claude Desktop, a custom app, or a CLI tool) to connect to and use them.

How many MCP servers can I connect at once?

Most clients support multiple simultaneous server connections. Claude Desktop, Cursor, and Cline all handle multiple servers well. The practical limit depends on your system resources and the servers’ resource usage.

Are MCP clients free?

Most MCP clients are free. Claude Desktop, Cline, Goose, and many others cost nothing to use. Some, like Cursor, have paid tiers for additional features. The LLM usage (API calls to Claude, GPT-4, etc.) may incur costs depending on your provider.

Next Steps

You understand what MCP clients are and which one fits your workflow. Here’s how to go further:

Set up your first client: Follow the Claude Desktop quickstart above, or try Jenova for the easiest possible setup
Explore available servers: Browse our MCP Server Directory to find servers for your use cases—we’ve catalogued 600+ servers across development, databases, productivity, and more
Learn MCP fundamentals: Read our What is Model Context Protocol? guide for deeper protocol understanding
Try development servers: Check out Development Tools MCP Servers for coding-focused options like GitHub, filesystem, and Git
Join the community: The MCP ecosystem is growing fast. Follow Anthropic’s MCP announcements and explore GitHub discussions for the latest developments

The AI assistant of the future isn’t just smart—it’s connected. MCP clients are how you make that connection.

Looking for MCP servers to connect to your client? Explore our complete directory of 600+ MCP servers, organized by category with GitHub stats, documentation links, and installation guides.

MCP Client: What It Is, How to Choose One, and Getting Started

MCP Client: What It Is, How to Choose One, and Getting Started

Quick Answer: Which MCP Client Should You Use?

What Is an MCP Client?

MCP Architecture: Where Clients Fit

What Makes MCP Clients Different from Direct API Calls?

How MCP Clients Work

Transport Methods

Connection Lifecycle

Types of MCP Clients

IDE-Integrated Clients

Desktop Applications

CLI and Terminal Clients

Web-Based and Browser Clients

Framework and SDK Clients

Best MCP Clients Compared

Feature Highlights

How to Choose the Right MCP Client

By Primary Use Case

By Technical Comfort Level

Checklist: Questions to Ask

Getting Started: Your First MCP Client Setup

Step 1: Install Claude Desktop

Step 2: Locate Your Configuration File

Step 3: Add Your First MCP Server

Step 4: Restart Claude Desktop

Step 5: Verify the Connection

Troubleshooting Common Issues

MCP Client Security Considerations

Authentication and Authorization

Permission Scoping

Data Exposure Risks

Enterprise Deployment

Building a Custom MCP Client

Python

TypeScript

When to build custom:

When to use existing clients:

Frequently Asked Questions

Next Steps

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