MCP Client is an AI application that connects to MCP servers to access external tools, data sources, and prompts during conversations.
How MCP clients work
An MCP client is the consumer side of the Model Context Protocol. It is the AI application you interact with, the one that connects to MCP servers to extend what the AI can do.
When a client starts up, it reads its configuration to discover which MCP servers are available. It connects to each one, performs a capability handshake, and learns what tools, resources, and prompts each server offers. From that point, the AI model has access to all of those capabilities during your conversations.
The important thing is that you do not need to tell the AI which tool to use. The client passes tool descriptions to the model along with your message, and the model decides whether to call a tool based on context. Ask Claude “what articles have I saved about React hooks?” and it will recognise that a bookmark search tool is relevant, call it, and incorporate the results.
Supported MCP clients
The list of AI applications supporting MCP grows steadily. The most established clients include:
- Claude Desktop was the first MCP client, released alongside the protocol in late 2024
- Claude Code supports MCP for developer workflows in the terminal
- Cursor integrates MCP into its AI-powered code editor
- Windsurf supports MCP as part of its AI coding environment
- Cline is an open-source VS Code extension with full MCP support
Each client implements MCP slightly differently in terms of configuration and UI, but the underlying protocol is the same. An MCP server that works with one client works with all of them.
Client configuration
Setting up MCP in a client typically involves editing a JSON configuration file or using a built-in settings UI. You specify the server’s command (for local servers) or URL (for remote servers) and any required environment variables like API keys.
For ContextBolt, the browser extension handles this automatically. It runs a local MCP server that AI clients can connect to. You add the server details to your preferred AI client’s configuration, and your bookmarks become searchable from within AI conversations.
Why the client matters
The client determines your experience with MCP. A well-implemented client makes tool use feel seamless, as though the AI naturally knows things it learned from connected servers. A poorly implemented client might surface tool calls awkwardly or fail to use available tools when they would be helpful.
The best MCP clients also handle practical concerns well: showing you when a tool is being called, letting you approve or deny tool use for security-sensitive operations, and gracefully handling cases where a server is unavailable.
As more AI applications adopt MCP, the value of every MCP server increases. Each new client means your existing tools, including ContextBolt, automatically reach more users without any additional integration work.