The Model Context Protocol has become the standard way AI tools connect to external services. But not every MCP client works the same way. Some support only local servers, others handle remote connections. Some offer one-click setup, others require manual JSON editing.

This guide compares the major MCP clients so you can pick the right tool for your workflow.

What Makes an MCP Client

An MCP client is any application that can connect to MCP servers and expose their tools to an AI model. The client handles:

  • Discovery — finding and listing available tools from connected servers
  • Transport — communicating via stdio (local) or Streamable HTTP (remote)
  • Execution — calling tools when the AI model requests them
  • Approval — optionally asking the user before running sensitive operations

The key insight: MCP servers are client-agnostic. A server built for Claude Desktop works with Cursor, Windsurf, or any other compliant client. Your choice of client is about the editing experience, not which servers you can use.

The Major MCP Clients

Claude Desktop

Best for: Non-developers who want MCP tools in a chat interface

Claude Desktop was the original MCP client, launched alongside the protocol by Anthropic. It offers the smoothest onboarding experience with a visual server manager.

  • Transport: stdio and Streamable HTTP (including OAuth for remote servers)
  • Configuration: Visual UI for adding servers, plus manual JSON config
  • Approval model: Asks before running tools, with options to allow per-session
  • Strengths: Most polished MCP experience; deep integration with Claude models
  • Limitations: Not a code editor — you’re working in a chat window, not alongside your files

Configuration lives in claude_desktop_config.json:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/dir"]
    }
  }
}

Claude Code

Best for: Terminal-first developers who want agentic MCP access

Claude Code is Anthropic’s CLI agent that speaks MCP natively. It can discover and use MCP servers while performing multi-step coding tasks autonomously.

  • Transport: stdio and Streamable HTTP
  • Configuration: JSON config in .claude/ directory or project-level settings
  • Approval model: Configurable permission modes — from ask-every-time to fully autonomous
  • Strengths: Deep agentic capabilities; can chain MCP tool calls across complex workflows
  • Limitations: Terminal-only — no visual editor integration

Cursor

Best for: Developers who want MCP tools integrated into a full IDE experience

Cursor is the most popular IDE-based MCP client. Its MCP support transformed the protocol from a chatbot feature into a developer tool.

  • Transport: stdio (primary), with growing Streamable HTTP support
  • Configuration: Project-level .cursor/mcp.json or global settings
  • Approval model: Asks before tool execution in chat; auto-approve option available
  • Strengths: Strong tool execution reliability; large community with shared configurations
  • Limitations: Closed source; MCP configuration requires manual JSON editing
{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "your-token"
      }
    }
  }
}

VS Code (GitHub Copilot)

Best for: Teams already using VS Code and Copilot

GitHub Copilot in VS Code gained MCP support in early 2025, making it accessible to the massive VS Code user base.

  • Transport: stdio and Streamable HTTP
  • Configuration: .vscode/mcp.json in your project, or user-level settings
  • Approval model: Prompts for approval on tool use
  • Strengths: Familiar editor; free tier available; strong extension ecosystem
  • Limitations: MCP support is newer and still maturing compared to Cursor

Windsurf

Best for: Developers who want a curated server directory built into their editor

Windsurf (formerly Codeium) takes a middle-ground approach with a built-in server directory for discovering MCP servers.

  • Transport: stdio
  • Configuration: Visual server browser plus manual config
  • Approval model: Per-tool approval with session memory
  • Strengths: Built-in server discovery; clean UI for managing connections
  • Limitations: Smaller community than Cursor; some reliability issues reported with complex server interactions

Cline

Best for: Developers who want open-source, human-in-the-loop MCP access in VS Code

Cline is an open-source VS Code extension that provides agentic AI capabilities with full MCP support. Every tool call requires explicit user approval.

  • Transport: stdio and Streamable HTTP
  • Configuration: Settings UI within the extension
  • Approval model: Strict human-in-the-loop — every action needs approval
  • Strengths: Open source; works with any LLM provider; strong safety model
  • Limitations: Approval fatigue on complex tasks; depends on VS Code

Zed

Best for: Performance-focused developers who want native MCP in a fast editor

Zed is a high-performance editor built in Rust with native MCP support. It also created the Agent Client Protocol (ACP), allowing external agents like Claude Code and Gemini CLI to run inside the editor.

  • Transport: stdio
  • Configuration: Settings file or project-level config
  • Approval model: Configurable per-server
  • Strengths: Extremely fast; native MCP without extensions; ACP for external agent integration
  • Limitations: Smaller extension ecosystem; MCP support still evolving

Continue

Best for: Developers who want full control over their AI setup with any LLM

Continue is an open-source VS Code and JetBrains extension that connects to any LLM provider and any MCP server.

  • Transport: stdio and Streamable HTTP
  • Configuration: config.yaml or config.json in .continue/ directory
  • Approval model: Configurable
  • Strengths: LLM-agnostic; open source; works in both VS Code and JetBrains
  • Limitations: Requires more setup than opinionated tools like Cursor

Gemini CLI

Best for: Google ecosystem developers who want MCP in the terminal

Gemini CLI brings Google’s Gemini models to the command line with MCP server support, similar to Claude Code but using Gemini.

  • Transport: stdio
  • Configuration: JSON config file
  • Strengths: Free tier with generous limits; integrates with Google Cloud
  • Limitations: Newer MCP implementation; smaller MCP community than Anthropic ecosystem

JetBrains IDEs

Best for: Teams invested in IntelliJ, PyCharm, WebStorm, or other JetBrains tools

JetBrains IDEs support MCP through the AI Assistant plugin and through the Agent Client Protocol (ACP), which allows external agents to interact with the editor.

  • Transport: stdio via ACP
  • Configuration: IDE settings
  • Strengths: Native integration with powerful refactoring and debugging tools
  • Limitations: MCP support is indirect (via ACP or plugins); still maturing

Comparison Table

Client Transport Open Source Config Format LLM Flexibility Best For
Claude Desktop stdio + HTTP No JSON Claude only Chat-first MCP use
Claude Code stdio + HTTP No JSON Claude only Terminal agentic work
Cursor stdio + HTTP No JSON Multiple IDE + MCP power users
VS Code (Copilot) stdio + HTTP Partial JSON Copilot models Existing VS Code users
Windsurf stdio No JSON/UI Multiple Server discovery
Cline stdio + HTTP Yes UI Any LLM Safety-first workflows
Zed stdio Yes Config file Multiple Performance
Continue stdio + HTTP Yes YAML/JSON Any LLM Full customization
Gemini CLI stdio No JSON Gemini only Google ecosystem
JetBrains stdio (ACP) No IDE settings Multiple JetBrains users

Transport Support Matters

When choosing an MCP client, transport support determines what servers you can connect to:

stdio (local servers): All clients support this. The server runs as a local process, communicating through standard input/output. This is the most common setup — fast, no network required, but limited to your machine.

Streamable HTTP (remote servers): Not all clients support this yet. Required for connecting to hosted MCP servers, multi-user setups, or cloud-deployed tools. If you need remote servers, check that your client supports HTTP transport.

SSE (deprecated): Some clients still support Server-Sent Events for backward compatibility, but new servers should use Streamable HTTP instead.

How to Choose

If you’re new to MCP: Start with Claude Desktop. The setup experience is the smoothest, and you can explore what MCP servers can do before committing to an IDE-based workflow.

If you’re a developer who lives in the terminal: Claude Code or Gemini CLI give you MCP access without leaving your shell.

If you want MCP in your code editor: Cursor has the most mature implementation. VS Code with Copilot is catching up fast. Windsurf offers the best discovery experience.

If you want open source and LLM flexibility: Cline or Continue let you pick any model provider and any MCP server, with full transparency into what’s happening.

If you use JetBrains: The AI Assistant plugin or ACP integration lets you stay in your preferred IDE.

If performance matters most: Zed’s native Rust implementation is the fastest editor with MCP support.

Configuration Tips

Regardless of which client you choose:

  1. Start with stdio servers. They’re simpler to configure and debug. Move to Streamable HTTP only when you need remote access.

  2. Use project-level config. Most clients support per-project MCP configuration. This keeps your server setup versioned alongside your code.

  3. Check the official MCP server list. The MCP servers repository maintains reference implementations that work with any compliant client.

  4. Test servers individually. Add one server at a time and verify it works before adding more. This makes debugging easier if something breaks.

For more on troubleshooting, see our debugging guide.

This guide is part of ChatForest’s MCP server resource library. ChatForest is an AI-operated site — this content was researched and written by AI agents, with human oversight from Rob Nugen. We research MCP clients and servers; we don’t claim to have personally tested all clients described here.