What is MCP Client?

What Is an MCP Client?

In the Model Context Protocol (MCP) specification introduced by Anthropic, the MCP Client is the host application that initiates connections, manages user authorizations, and orchestrates interactions between a Large Language Model (LLM) and external data sources.

Before MCP, connecting an AI model to an external system required custom, one-off integrations: writing unique API wrappers for GitHub, custom SQL connectors for Postgres, and proprietary scraping hooks for Slack. If you switched AI assistants, every single integration had to be rewritten from scratch.

The Model Context Protocol formalizes this relationship into a clean Client-Server architecture:

• MCP Server: Exposes standardized resources (data), tools (actions), and prompt templates.
• MCP Client: The host application where the user interacts with the AI (e.g. Claude Desktop, Claude Code, Cursor IDE, or Windsurf), responsible for negotiating protocol capabilities, validating permissions, and routing model tool invocations.

┌─────────────────────────────────────────────────────────────────────────────┐
│                    MCP CLIENT-SERVER ARCHITECTURE                           │
├─────────────────────────────────────────────────────────────────────────────┤
│                               USER PROMPT                                   │
│                                    │                                        │
│                                    ▼                                        │
│                         ┌───────────────────────┐                           │
│                         │       MCP CLIENT      │                           │
│                         │ (Claude, Cursor, etc) │                           │
│                         └──────────┬────────────┘                           │
│                                    │ JSON-RPC 2.0 (stdio / SSE)             │
│            ┌───────────────────────┼───────────────────────┐                │
│            ▼                       ▼                       ▼                │
│   ┌─────────────────┐    ┌──────────────────┐    ┌───────────────────┐      │
│   │ GitHub MCP      │    │ Database MCP     │    │ Memora Memory MCP │      │
│   │ (Commits & PRs) │    │ (Postgres & SQL) │    │ (Knowledge Graph) │      │
│   └─────────────────┘    └──────────────────┘    └───────────────────┘      │
└─────────────────────────────────────────────────────────────────────────────┘

How an MCP Client Operates: The Protocol Lifecycle

An MCP Client manages four critical phases in its interaction with connected servers:

1. Connection Initialization & Capability Handshake

The client establishes communication with the server over either local standard input/output (stdio) or remote HTTP Server-Sent Events (SSE). The client sends an initialize JSON-RPC request:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "initialize",
  "params": {
    "protocolVersion": "2024-11-05",
    "capabilities": {
      "roots": { "listChanged": true },
      "sampling": {}
    },
    "clientInfo": {
      "name": "memora-client",
      "version": "1.0.0"
    }
  }
}

The server responds with its supported capabilities, including available resources, tools, and prompts.

2. Dynamic Tool Discovery & Registration

The MCP client sends a tools/list request to discover executable operations. Tools include JSON schemas defining required inputs, return types, and descriptions that guide the LLM's tool-calling engine.

3. Execution & Human-in-the-Loop Security

When the LLM decides to invoke a tool (e.g. read_pr_diff or query_jira_issue), the MCP client intercepts the call:

• Enforces user permission boundaries (e.g. prompting the user: "Allow Claude to run tool X?").
• Formats parameters according to the JSON-RPC spec.
• Dispatches the tools/call message to the appropriate server.

4. Sampling & Reverse Inference

Advanced MCP Clients support Sampling, allowing an MCP server to request that the client LLM analyze data or summarize content within the client's existing security boundary.

Technical Comparison: MCP Client vs MCP Server

Configuring an MCP Client for Enterprise Context

In popular AI editors like Claude Desktop or Cursor, users configure an MCP client via a local configuration file (such as claude_desktop_config.json):

{
  "mcpServers": {
    "memora-memory": {
      "command": "npx",
      "args": ["-y", "@memora/mcp-server@latest"],
      "env": {
        "MEMORA_API_KEY": "mem_live_sec_12345"
      }
    }
  }
}

Once loaded, the AI assistant inside the client gains real-time access to company documentation, historical PR rationales, and Slack architectural decisions.

Frequently Asked Questions

What are the most common MCP Clients available today?

Anthropic's Claude Desktop app, Cursor AI code editor, Windsurf by Codeium, Claude Code, Zed editor, and custom enterprise orchestrators are common examples of production MCP Clients.

Can an application be both an MCP Client and an MCP Server?

Yes. Some systems act as both: an orchestrator can consume tools from other MCP servers while exposing its own tools to other clients. Memora, for example, provides an MCP Server that lets clients like Claude Code and Cursor query knowledge from your connected tools.

Is communication between MCP Clients and Servers encrypted?

When communicating locally via standard I/O (stdio), data stays strictly within the user's local operating system boundary. When communicating remotely over Server-Sent Events (SSE), industry-standard TLS encryption and OAuth bearer authentication are mandatory.