What is an MCP Server? Explained Simply

Model Context Protocol (MCP) explained – how it works, server types, security and FAQ.

Skill Road
What is an MCP Server? Explained Simply

An MCP server is a service that connects an AI application to external tools and data through the Model Context Protocol (MCP). It lets MCP clients such as Claude Code or Cursor use files, databases, APIs and other services through a standardized interface.

MCP server explained simply

Think of an MCP server as a standardized bridge between an AI assistant and another service. The AI client discovers which tools the server provides, calls the appropriate tool when needed, and receives the result back into its context.

User
AI client
MCP server
External service / data

What is MCP?

The Model Context Protocol (MCP) is an open standard published by Anthropic in 2024. It defines how AI assistants and agents – such as Claude, Cursor or other MCP clients – communicate with external services, data sources and tools. An MCP server is the counterpart: a service that exposes tools to the AI client via the protocol. The client can invoke these tools without knowing the implementation details of the service.

How does an MCP server work?

The protocol follows a client-server model with four steps:

The MCP client (e.g. Claude Code, Cursor) connects to one or more MCP servers on startup.
The server announces which tools it offers – for example "read file", "query database" or "send email".
Based on the user's request, the AI model decides which tool to call and the client executes the call.
The result of the tool call flows back into the model's context, which uses it to formulate the response.

Why MCP instead of direct API integration?

Before MCP, every AI application had to integrate every external service individually – custom code, custom authentication, custom error handling. MCP standardises this interface: any MCP-capable client can use any MCP server without custom adapter code. This significantly reduces development effort and makes it possible to use the same MCP server with different AI clients.

Local and remote MCP servers

MCP servers can be distinguished by how they are deployed:

Remote (hosted): The server runs at the provider and is reachable over HTTPS. Typical for official SaaS integrations such as Stripe, Notion or GitHub. No local installation required, but OAuth or API key authentication is needed.
Local (STDIO): The server runs as a process on the user's machine and communicates via standard input/output. Typical for filesystem, code or developer tools. Full control, but setup and updates are the user's responsibility.

Which AI clients support MCP?

MCP is supported by a growing number of AI clients. Well-known examples include Claude Code (Anthropic), Cursor, Windsurf, Codex CLI (OpenAI) and Claude Desktop. Compatibility is listed for every catalog entry on Skill Road.

Find suitable MCP servers for Claude Code and Cursor or browse the MCP server directory.

Security and privacy

MCP servers can access external services, read data and, depending on configuration, also write or delete data. Key rules: only use officially reviewed servers, store credentials (API keys, OAuth tokens) securely and never in plaintext in configuration files, and require user confirmation before executing write operations. Data returned by an MCP server can flow into the language model context of the client – review the privacy policies of both sides.

Frequently asked questions about MCP

Is MCP an open standard?

Yes. MCP was published by Anthropic as an open specification and is being developed further by multiple providers and communities. The specification is publicly available.

Do I need programming skills to use MCP servers?

For remote servers, generally not – you enter the server URL and an API key in your MCP client. For local servers, a simple install command (npm, pip, npx) is usually all that is needed. Writing your own MCP servers does require programming knowledge.

What is the difference between an MCP server and a plugin?

An MCP server exposes tools via the standardised Model Context Protocol and can be used with any MCP-compatible client. A plugin is a platform-specific extension for a particular client or application and is not easily portable.