Which MCP Clients Are Available and How Do They Compare?

MCP clients are applications that connect to MCP servers and let AI models call external tools through the Model Context Protocol (MCP). Anthropic created the protocol. Claude Desktop was the first MCP client. As of mid-2026, over 15 significant clients support MCP across desktop apps, IDEs, CLI tools, and web interfaces.

This guide compares every significant MCP client by transport support, feature coverage, and configuration method. It names the editorial pick per category and links to dedicated setup guides. No vendor publishes this comparison because each vendor documents only its own client.

What Is an MCP Client?

An MCP client is an application that connects to MCP servers, discovers their tools, and lets an AI model call them. The client sits between the model and the server. It handles transport, authentication, and tool discovery.

The model reasons and decides which tools to call. The server exposes those tools. The client connects the two. Without a client, the model has no way to reach external tools, databases, or services.

Every MCP client performs three functions. It discovers which tools a connected server exposes. It formats the model’s tool calls into JSON-RPC messages the server understands. It returns the server’s responses in a format the model can reason over.

What Is the Difference Between an MCP Client and an AI Agent?

An MCP client is the connection layer that discovers and calls server tools. An AI agent is the reasoning layer that decides which tools to call and in what order. Claude Desktop is a client. The Claude model running inside it is the agent. The client provides the plumbing. The agent provides the intelligence. Claude Code blurs this line by acting as both client and agent.

What Is the Difference Between an MCP Client and an LLM Client?

An LLM client is any application that calls a language model API. An MCP client is an LLM client that also connects to MCP servers for tool access. All MCP clients are LLM clients. Not all LLM clients are MCP clients. A Python script calling the OpenAI API is an LLM client. It becomes an MCP client only when it also connects to MCP servers through the protocol.

Which Features Matter When Choosing an MCP Client?

Five features differentiate MCP clients. Transport support determines which servers you can connect to. MCP primitive support determines what those servers can do. Tool loading strategy determines token cost. Config method determines setup complexity. Pricing determines access.

1. Transport support. Stdio connects to local servers running on your machine. The client launches the server as a child process. No network involved. Streamable HTTP connects to remote hosted servers. The server runs on someone else’s infrastructure (GitHub’s hosted MCP endpoint, Cloudflare Workers, Supabase). SSE is the legacy remote transport from the November 2024 spec. Clients that dropped SSE support cannot connect to servers that only offer SSE. A client that supports only stdio cannot connect to any remote server. A client that supports both stdio and HTTP can connect to all server types. This is the single most important differentiator.

2. MCP primitive support. All clients support tools (actions the model can execute). Not all support resources (read-only data the model can access), prompts (reusable templates the server provides), or sampling (model access from the server side). A client that supports only tools misses resources entirely. A Figma server that exposes design tokens as resources is invisible to a tools-only client. A server that provides specialized prompt templates for code review cannot surface them to a client without prompt support.

3. Tool loading strategy. Most clients load every tool definition from every connected server at session start. Five servers with 20 tools each means 100 tool definitions loaded before the user types a word. Each definition costs 300 to 500 tokens. Total: 30,000 to 50,000 tokens consumed on tool definitions alone. Claude Code solves this with lazy tool loading through Tool Search. It discovers tools on demand. It loads only the definitions the current task needs. This reduced main-thread token consumption by 47% in independent testing (Joe Njenga, 2026). No other client offers this as of mid-2026.

4. Config method. Claude Desktop and Cursor use JSON config files. The file path differs by client and operating system. Claude Desktop: claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\). Cursor: .cursor/mcp.json in the project root. VS Code: .vscode/mcp.json or UI settings. Claude Code uses a CLI command (claude mcp add) that writes the config automatically. ChatGPT uses an in-app settings flow with no config file. The config method determines how fast you can add, remove, and test servers. CLI commands are fastest. UI flows are easiest. JSON files offer the most control.

5. Pricing. Claude Desktop is free with usage limits. Cursor Pro is $20/month. VS Code with GitHub Copilot is $10/month. Windsurf Pro is $20/month. Claude Code bills per token through the Anthropic API (variable cost based on usage). ChatGPT requires a Plus subscription ($20/month). Cline and Zed are free. The pricing model matters for teams: per-seat subscriptions (Cursor, Windsurf) vs per-token billing (Claude Code) vs free-with-limits (Claude Desktop, Gemini CLI).

Which MCP Clients Are Available by Category?

Over 15 significant MCP clients exist across four categories. Desktop and AI assistants serve general-purpose use. IDEs serve coding workflows. CLI tools serve terminal-first development. Web and open-source projects serve custom and self-hosted deployments.

The category determines the workflow, not the capability. A desktop client and an IDE client connect to the same servers. They run the same tools. The difference is where you work. A developer who lives in the terminal picks Claude Code. A developer who lives in VS Code picks GitHub Copilot Chat. A non-developer who needs AI with tool access picks Claude Desktop. The servers are the same. The client is the workspace.

Each capsule below lists transport support, pricing, and the specific “use when” recommendation. T1 clients have dedicated guides on this site with full setup walkthroughs.

Desktop and AI Assistants

• Claude Desktop is the canonical MCP client. Built by Anthropic. Config: claude_desktop_config.json. Transport: stdio and HTTP. Supports tools, resources, and prompts. Free tier with usage limits. Use for general-purpose AI assistance with MCP server access. The config file path differs by OS: ~/Library/Application Support/Claude/ on macOS and %APPDATA%\Claude\ on Windows. The most common first-time error: editing the config while Claude Desktop is running. The app reads the config at launch. Edits require a full quit and relaunch.
• ChatGPT Desktop adopted MCP in April 2025. Config: in-app settings. Transport: stdio only. Supports tools. Requires ChatGPT Plus ($20/month). Use when your workflow requires OpenAI models. The MCP experience is smoother in Claude Desktop.
• Raycast integrates MCP through launcher commands. macOS only. Use for launcher-based productivity workflows.

Integrated Development Environments

• Cursor is a VS Code fork with deep MCP integration. Config: .cursor/mcp.json in the project root. Transport: stdio and HTTP. Supports tools and resources. $20/month Pro. Use for AI-assisted coding in a dedicated IDE. Cursor and Claude Code share the same server packages. A server configured for one works in the other. The config file in the project root means server configurations travel with the repository. Clone the repo and the MCP setup comes with it.
• VS Code supports MCP natively since version 1.99 (early 2026) through GitHub Copilot Chat. Config: .vscode/mcp.json. The JSON root key is "servers", not "mcpServers". Every developer who copies a config from Claude Desktop hits this error first. Claude Desktop uses "mcpServers". VS Code uses "servers". The server entries inside are identical. Only the root key differs. Transport: stdio and HTTP. $10/month with GitHub Copilot. Use for MCP inside an existing VS Code setup.
• Windsurf (Codeium) supports MCP across 40+ IDEs including JetBrains and Vim. FedRAMP and HIPAA certified. $20/month Pro. Use when enterprise compliance requirements matter.
• Zed has built-in MCP support. Free. Use for a lightweight, fast editor with MCP.
• Replit has built-in MCP support. Use for cloud-based development environments.

CLI and Terminal Tools

• Claude Code is the most capable MCP client as of mid-2026. Three features set it apart. First, it supports three scope levels: project (servers available only in one project), user (available across all projects for one user), and global (available system-wide). Second, it acts as both MCP client and MCP server, meaning other tools can connect TO Claude Code. Third, it uses Tool Search to lazy-load tool definitions on demand instead of loading all tools at session start. A developer with 10 servers connected in other clients loads 100+ tool definitions at every session start. Claude Code loads only the 3 to 5 tools the current task needs. Config: claude mcp add command. Transport: stdio and HTTP. Bills per token through the Anthropic API. Use for terminal-first development with maximum MCP capability.
• Codex CLI is OpenAI’s terminal coding tool with MCP support. Use when your workflow requires OpenAI models in the terminal.
• Gemini CLI is Google’s terminal tool with MCP support. Free with usage limits. Use when your workflow requires Gemini models.
• Cline is an open-source coding agent running inside VS Code. Supports bring-your-own-key with broad MCP support. Free. Use for open-source, vendor-neutral MCP access.
• GitHub Copilot CLI supports MCP for terminal-based GitHub workflows.

Web, Open-Source, and Local LLM Clients

• Goose is Block’s open-source AI agent framework. A founding project of the Agentic AI Foundation alongside MCP. Local-first. Supports MCP natively. Use for a fully open-source agent with MCP integration.
• Ollama does not natively support MCP as of mid-2026. GitHub issue #7865 is still open. Bridge tools exist: MCPHost (Go-based, connects Ollama to MCP servers natively), ollama-mcp-bridge, and ollmcp (interactive terminal UI with human-in-the-loop controls). These bridges connect Ollama’s local models to MCP servers through an intermediary layer. Simple single-tool workflows (filesystem access, basic search) work with capable local models like Qwen 3.5 and Gemma 4. Complex multi-step tool chains require frontier-class reasoning that most local models do not yet match. Start with the Filesystem server and a 14B+ parameter model.
• llama.cpp merged full MCP client support into its built-in web UI in March 2026. GGUF models with tool-calling support connect to MCP servers directly. No bridge needed. This was a milestone for local, fully private MCP deployments.
• Open WebUI supports MCP with OAuth 2.1 and dynamic client registration for remote servers. Any model loaded in Open WebUI with tool-calling support can use connected MCP servers. Supports both Ollama-hosted and cloud API models through the same interface.

Which MCP Clients Are Most Used Right Now?

The most-used MCP clients by ecosystem signals in mid-2026 are Claude Desktop, Cursor, Claude Code, VS Code with GitHub Copilot, and Windsurf.

Claude Desktop remains the default for general-purpose MCP use. It is free, supports the most MCP primitives (tools, resources, prompts), and provides the smoothest onboarding for first-time MCP users. Cursor dominates AI-assisted coding in a dedicated IDE. Its MCP config lives in the project root, making server configurations portable across machines. Claude Code is the most capable terminal client. Its lazy tool loading, three scope levels, and dual client/server role make it the choice for complex multi-server workflows. VS Code with GitHub Copilot is the most common path for developers who prefer their existing editor and do not want to switch to a dedicated AI IDE.

The growth pattern shows IDEs overtaking desktop apps. Developers spend more time in their editor than in a chat window. MCP support inside the IDE means servers connect where the work happens. The trend toward remote servers (streamable HTTP) also favors IDE clients that support both local and remote transports.

You know which clients exist and what differentiates them. The next step is choosing the one that fits your workflow and connecting your first server.

What Happens When You Use an AI App Without MCP Support?

Without MCP support, an AI application is limited to its training data and whatever the user pastes into the conversation window. No live database queries. No real-time file access. No tool execution.

The user becomes the integration layer. Want the model to review a GitHub PR? Copy the diff from GitHub, paste it into the chat, wait for the response, copy the review comments back to GitHub. Want the model to query a database? Run the query manually, copy the results, paste them into the chat. Want the model to check a web page? Take a screenshot, upload it, describe what you want. Every interaction requires manual context transfer.

This workflow breaks at scale. A developer who checks 5 PRs, queries 3 databases, and reads 10 files per day performs 18 manual copy-paste cycles. Each cycle loses context. Each switch costs time. Each paste risks truncation.

An MCP client eliminates this. The model reads files directly. It queries databases directly. It calls APIs directly. The user describes the task. The model executes it through the servers the client connects to.

How MCP differs from APIs

MCP Client Questions

What Is an MCP Client?

An MCP client is an application that connects to MCP servers. It lets an AI model discover and call external tools through the Model Context Protocol. Claude Desktop, Cursor, VS Code, Claude Code, and ChatGPT are all MCP clients.

Are MCP Clients Free?

Most MCP clients offer a free tier. Claude Desktop is free with usage limits. VS Code is free (GitHub Copilot is $10/month). Zed is free. Cline is free and open-source. Cursor and Windsurf charge $20/month for Pro. Claude Code bills per token.

Is an MCP Client the Same as an AI Agent?

No. An MCP client is the connection layer. An AI agent is the reasoning layer. The client discovers tools and sends messages. The agent decides which tools to call, interprets results, and plans next steps. Some products combine both in one interface.

Which Companies Use MCP?

Anthropic, OpenAI, Google, Microsoft, Block, GitHub, Cloudflare, Stripe, Supabase, Notion, Figma, and Slack all support MCP as of mid-2026. Anthropic, OpenAI, and Block co-founded the Agentic AI Foundation to govern the protocol. Google, Microsoft, and AWS participate as members.