According to the article, what are Agent Cards in the MCP protocol?

Machine-readable descriptions that let agents dynamically discover available tools and how to use them

Physical hardware tokens that authenticate AI agents

Machine-readable descriptions that let agents dynamically discover available tools and how to use them

Visual dashboards showing agent performance metrics

Configuration files that limit which users can access an agent

🔌 MCP is doing for AI agents what HTTP did for the web — here's why it matters

The Model Context Protocol (MCP) is rapidly becoming the universal standard for connecting AI agents to external tools and data. Think of it as a USB-C port for AI — one standard interface that works with everything.

Before MCP, every agent-tool integration was custom. Want your agent to read Jira tickets? Write a custom integration. Google Drive? Another one. Slack? Another. Each with its own auth handling, error formats, and data schemas. It didn't scale.

MCP standardizes this with Agent Cards — machine-readable descriptions that let agents dynamically discover what tools are available and how to use them. Agents can browse a registry of MCP servers the way browsers discover web services. The protocol uses structured JSON payloads for robust communication.

The network effect is kicking in: as more tools expose MCP servers, agents become more capable, which drives more tools to adopt MCP. GitHub, Google, Stripe, and dozens of others now offer MCP servers. Karpathy predicts that agent traffic will compound toward agent-native infrastructure — companies that are easy for agents to use will win over those optimized only for humans.