An MCP (Model Context Protocol) server is a standalone process that exposes tools, resources, and prompts to AI agents via a standardized JSON-RPC protocol.
MCP (Model Context Protocol) servers are standalone processes that expose capabilities to AI agents through a standardized protocol developed by Anthropic. Each MCP server runs as its own process, communicating with AI agents via JSON-RPC over stdin/stdout or HTTP. MCP servers can provide tools (executable functions), resources (data sources), and prompts (reusable templates). This separation of concerns allows AI agents to access external systems — databases, APIs, file systems — through a unified interface.
While both extend AI agent capabilities, they take fundamentally different approaches: - MCP servers run as separate processes with OS-level isolation. They're language-agnostic and can be written in Python, TypeScript, Rust, etc. - OpenClaw skills are declarative SKILL.md files that run within the OpenClaw process. They're simpler to install and audit but less isolated. For a detailed comparison, see our MCP vs Skills article.
MCP servers are ideal when you need: - Process isolation — The tool runs in its own sandbox - Language flexibility — Write server logic in any language - Shared infrastructure — Multiple AI agents can connect to the same MCP server - Stateful operations — Long-running connections to databases or APIs
Yes. OpenClaw supports MCP servers as tool providers alongside native skills. You can use both simultaneously in the same agent configuration.
MCP servers provide process-level isolation, which limits the blast radius of vulnerabilities. However, skills offer transparency through auditable SKILL.md declarations. Both approaches have trade-offs.
MCP servers are typically installed via npm (npx) or pip, then configured in your AI client's MCP settings file. The setup varies by server and client.