Model Context Protocol (MCP)

Before MCP, every AI application built its own custom integrations. If you wanted your AI agent to read from Slack, query a database, and update Jira, you had to write separate integration code for each one. Every model provider had a different way of handling tool connections. This meant a lot of duplicated work.

MCP changes this by defining a standard protocol. A tool provider implements the MCP server specification once, and any MCP-compatible AI application can use that tool. An AI application implements the MCP client specification once, and it can connect to any MCP-compatible tool. This is the same pattern that made the web work: standardize the protocol, and everything becomes interoperable.

The protocol covers three main capabilities: tools (functions the model can call), resources (data the model can read), and prompts (templates for common interactions). A single MCP server might expose all three. For example, a CRM integration might provide tools for creating and updating records, resources for reading customer data, and prompts for common sales tasks.

For enterprise AI development, MCP reduces integration cost and increases flexibility. You can swap AI models without rebuilding your tool integrations. You can add new tools without modifying your AI application's core logic. The ecosystem of pre-built MCP servers is growing, covering databases, SaaS tools, file systems, and more.

MCP is still relatively new, but adoption is accelerating. Major AI providers and tool builders are implementing support. For teams building AI agents, designing around MCP from the start means your integrations will be more portable and future-proof.