MCP (Model Context Protocol)
MCP is an open standard for connecting AI models to tools and data sources through a common interface, so the same connector works across different AI apps. Think of it as a universal adapter: build a server once and any MCP-compatible assistant can use it, instead of writing a custom integration per app.
Before MCP, every AI app wired up tools and data in its own way, so each integration was bespoke and non-portable. MCP defines a shared protocol where an MCP server exposes tools, resources, and prompts, and any MCP client (an AI app or agent) can discover and call them. The payoff is reuse: connect your codebase, your database, your ticketing system once, and multiple AI tools can talk to it.
When building a product, MCP matters in two directions. If you want your service usable from AI assistants, exposing an MCP server makes you a first-class citizen in that ecosystem. If you are building an agent, consuming MCP servers lets you plug in capabilities without writing each integration from scratch. The same security caution applies as with any agent tooling — an MCP server is a privileged surface, so authentication, scoping, and guarding against prompt injection through tool results are part of doing it properly.
// faq
Frequently asked questions
- Why does MCP matter for my product?
- Two reasons. Exposing an MCP server makes your product directly usable from AI assistants and agents, without each of them building a custom integration. And if you're building an agent yourself, consuming existing MCP servers lets you add capabilities — files, databases, third-party APIs — through one standard interface instead of bespoke code per source. It turns integration from N×M custom work into reusable connectors.