MCP Glossary
Key terms and concepts from the Model Context Protocol ecosystem. Each entry explains what it means, why it matters, and how it connects to the broader MCP architecture.
Model Context Protocol (MCP)
Model Context Protocol (MCP) is an open standard that defines how AI assistants connect to external tools, data sources, and services. It provides a structured way for AI models to discover, understand, and use capabilities from any MCP-compatible server.
MCP Tools
MCP Tools are the action primitive in the Model Context Protocol. They let AI assistants perform operations — creating records, triggering deployments, sending messages, or any other action your service supports — by calling structured functions on your MCP server.
MCP Resources
MCP Resources expose read-only data to AI assistants. They let AI models query databases, read files, fetch configurations, or access any data from your system — without performing mutations or side effects.
MCP Prompts
MCP Prompts are guided workflows that an MCP server exposes to AI assistants. They define multi-step processes — like onboarding a new user, debugging an issue, or generating a report — that combine your tool's capabilities with structured AI guidance.
MCP Server
An MCP server is a service that implements the Model Context Protocol to expose tools, resources, and prompts to AI assistants. It handles connection management, capability negotiation, and request routing between AI clients and your underlying systems.
MCP Client
An MCP client is an AI assistant or application that connects to MCP servers to discover and use their capabilities. Popular MCP clients include Claude, Cursor, GitHub Copilot, and other AI-powered developer tools.
MCP Transport
MCP transports define how AI clients communicate with MCP servers. The protocol supports two transport types: HTTP (for cloud-deployed servers accessible over the internet) and STDIO (for local servers that communicate through standard input/output).
HTTP Transport
HTTP transport allows MCP servers to communicate with AI clients over standard HTTP connections. This is the recommended transport for production MCP servers that need to be accessible from cloud-hosted AI assistants and services.
STDIO Transport
STDIO transport allows MCP servers to run as local processes that communicate with AI clients through standard input (stdin) and standard output (stdout). This is ideal for developer tools, CLI integrations, and local-first AI workflows.
Tool Calling
Tool calling (also called function calling) is the mechanism by which AI models invoke external functions or APIs. MCP standardizes tool calling across AI providers, making it consistent, discoverable, and secure rather than provider-specific.
xmcp
xmcp is a TypeScript framework for building Model Context Protocol (MCP) servers. It provides type-safe tool definitions, built-in authentication, HTTP and STDIO transport support, and one-command Vercel deployment.
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