MCP, explained for founders: what it is and whether you need it

MCP, the Model Context Protocol, is an open standard for connecting AI assistants and agents to outside tools and data. Anthropic introduced it in November 2024, and it is now supported across the major AI assistants. Think of it as one plug shape: build the connection once, and any assistant that speaks MCP can use it.

What an MCP server exposes

Your product or internal system runs the MCP server. The assistant or agent is the client. The server decides what the AI may see and do.

• Tools: actions the AI can take, such as creating an invoice, searching orders or booking a slot.
• Resources: data the AI can read, such as documents, records or a product catalogue.
• Prompts: reusable instructions for common jobs.

When it is worth building one

• You sell software and customers want to use it from ChatGPT, Claude or their own agents. An MCP server makes your product usable where they already work.
• You are building several agents that need the same internal systems. One MCP server beats five custom integrations.
• Your team wants an internal copilot over your own data and tools, with access controlled in one place.

When it is not

If you are building one agent that talks to one system, a direct integration is simpler and has fewer moving parts. MCP pays off when the same tools are used by more than one AI, or by AI you do not control.

Security comes first

An MCP server is a door into your systems for software that follows instructions in text. That makes it a target for prompt injection: a document or message that tells the AI to do something it should not. The OWASP Top 10 for LLM applications lists it first. So the server gets the same care as a public API:

• Authentication on every request, with permissions per user, not one shared key.
• Read-only by default. Every action that writes, pays or deletes is listed and approved.
• Every tool call logged with who asked, what ran and what came back.
• Limits on how much data one call can return.

The founder's short version

MCP is plumbing, not a product. It matters if AI tools will use your software or you run more than one agent. If neither is true yet, build the agent first and add MCP when a second AI needs the same tools.