MCP & context engineering
What Is MCP?
A growing standard for connecting AI agents to tools, without a custom integration for every pairing.
The Model Context Protocol (MCP) is an open standard that defines a common way for AI applications to connect to external tools, data sources, and services, so that any compliant AI client can talk to any compliant tool server without a custom integration for every combination.
Before standards like this, connecting an AI agent to a new tool or data source usually meant writing bespoke integration code for that exact pairing. MCP aims to make that connection reusable across tools and AI applications.
Key takeaways
The problem MCP addresses
Every tool an AI agent might need — a database, a calendar, a search engine, an internal system — historically required its own custom integration code, written specifically for that AI application and that tool.
As the number of tools and AI applications both grow, that approach does not scale: every new pairing needs new glue code. A shared protocol lets any compliant client connect to any compliant server without bespoke work each time.
How MCP is structured
An MCP server exposes a set of capabilities — tools it can run, resources it can provide, prompts it supports — in a standard, discoverable format. An MCP client, often an AI agent or assistant, connects to one or more servers and can then use those capabilities as part of its reasoning and actions.
This client-server structure means a single tool provider can build one MCP server and have it work across many different AI applications that support the protocol.
What MCP means for AI engineers
In practice, MCP reduces integration work: instead of writing a custom connector for every tool your agent needs, you can connect to existing MCP servers, or expose your own systems through an MCP server for others to use.
It is still an emerging standard, and understanding its structure now positions AI engineers to work efficiently as more tools and platforms adopt it.
Put this into practice