What Is Model Context Protocol (MCP)? The Universal Interface for AI Agents
MCP is the USB-C of AI: a universal interface between agents and tools. Here is how it works.
Model Context Protocol (MCP) is an open standard created by Anthropic that defines how AI agents connect to external tools and data sources. It is the USB-C of AI: a universal interface that lets any compatible agent talk to any compatible tool without custom integration code for each combination.
Quick Answer: Model Context Protocol (MCP) is an open standard that lets AI agents connect to external tools, databases, and APIs through a universal interface. Instead of building custom integrations for each tool, developers build one MCP server and every compatible agent can use it. MCP servers exist for GitHub, Slack, Notion, Supabase, and hundreds of other tools. Agensi's marketplace connects to AI agents via MCP at agensi.io/mcp.
Before MCP, connecting an AI agent to a tool like GitHub required custom code for that specific agent. Connecting the same tool to a different agent required writing the integration again from scratch. MCP eliminates this duplication. Build one MCP server for GitHub, and every MCP-compatible agent (Claude Code, Cursor, Codex CLI, and 20+ others) can use it.
How does MCP work?
MCP uses a client-server architecture. The AI agent is the client. External tools are servers. The protocol defines three capabilities: Tools (functions the agent can call), Resources (data the agent can read), and Prompts (templates for common interactions). The agent discovers available tools by connecting to the server, then calls them as needed during its workflow.
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What AI agents support MCP?
As of 2026, MCP is supported by Claude Code, Cursor, Codex CLI, Cline, Windsurf, Continue, OpenCode, Gemini CLI, and 20+ other agents. The every-ai-agent-that-supports-skill-md article on Agensi tracks the full list.
What MCP servers are available?
Hundreds. The most popular: GitHub (code management), Slack (messaging), Notion (documents), Supabase (database), Playwright (browser automation), PostgreSQL (database queries), and Figma (design). Agensi maintains guides for the most-used servers: GitHub MCP, Supabase MCP, Slack MCP.
How does MCP relate to SKILL.md?
MCP connects agents to tools (what the agent CAN do). SKILL.md defines workflows (what the agent SHOULD do). They complement each other. A SKILL.md skill might instruct the agent to use the GitHub MCP server to create a pull request as part of a code review workflow. Learn more about how MCP and SKILL.md work together.
How do I set up MCP?
Most agents have built-in MCP support. In Claude Code, add a server to your configuration:
{
"mcpServers": {
"github": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": { "GITHUB_TOKEN": "your-token" }
}
}
}
The agent discovers the server's tools automatically and uses them when relevant to your request.
Can I build my own MCP server?
Yes. MCP servers can be built in TypeScript or Python. Anthropic provides SDKs for both languages. If you have built a useful MCP server, you can list it on Agensi and earn revenue from other developers who use it. See the build an MCP server tutorial.