Model Context Protocol (MCP)
An open standard that lets AI models connect to outside data sources and tools in a consistent way.
Quick Answer
Model Context Protocol (MCP) is an open standard, introduced by Anthropic in 2024, that lets AI applications connect to external data sources, tools, and systems through a common interface. Instead of building a custom integration for every data source, a developer builds one MCP connection that any compatible AI application can use to read files, query databases, or take actions.
The Full Picture
Before MCP, connecting an AI model to any given piece of software — a calendar, a database, a design tool — meant building a bespoke integration for that specific pairing. Every new AI application needed its own version of each integration, and every new data source needed to be wired up again for each AI tool that wanted to use it: an integration problem that multiplied with every new tool on either side.
MCP defines a standard client-server model: an 'MCP server' exposes a specific tool or data source — files, a database, a search engine — in a consistent format, and any 'MCP client,' typically an AI application, can connect to it the same way. It's often compared to a USB-C port: one standard connector that many devices can plug into, instead of a different cable for every combination.
In practice, a developer might build an MCP server for a company's project-document repository. Any MCP-compatible AI assistant can then read and search those documents without a custom integration being built for that specific assistant — the integration work happens once, on the data-source side, rather than repeatedly for every AI tool that wants access.
In construction software, this kind of standard connection is the mechanism that would let an AI assistant pull live data from a project's drawings, specs, or scheduling system rather than working only from files someone manually uploads. It's still a more common pattern in general software development today than in AEC-specific tools, most of which are earlier in adopting it.
Real Examples
Common Misconceptions
People assume: MCP is a specific AI model.
Actually: MCP is a connection protocol, not a model — it defines how an AI application talks to outside tools and data, similar to how HTTP defines how a browser talks to a website. The model doing the reasoning is separate from the protocol connecting it to data.
People assume: MCP only works with one company's AI.
Actually: MCP is an open, published standard, and AI applications and development tools beyond its original publisher have adopted it — that's the point of a standard: one connection pattern usable across different AI clients, rather than a proprietary integration locked to a single vendor.
Frequently Asked Questions
What is Model Context Protocol (MCP)?
An open standard, published by Anthropic in 2024, that defines a consistent way for AI applications to connect to external data sources and tools — files, databases, search engines, and more — instead of every AI tool and data source needing a custom, one-off integration with each other.
How does MCP work?
It uses a client-server model: an 'MCP server' exposes a tool or data source in a standard format, and an 'MCP client,' typically the AI application, connects to it the same way it would connect to any other MCP server, regardless of what's on the other end.
Who created MCP?
Anthropic introduced Model Context Protocol as an open standard in November 2024, releasing the specification, SDKs, and a set of example integrations, with adoption since extending to other AI applications and development tools beyond Anthropic's own products.
What's the difference between MCP and an API?
An API is a specific integration point defined by whoever built it, and connecting to a new one usually means writing new code. MCP is a standard pattern for exposing tools and data so many different AI applications can connect the same way, reducing how much bespoke integration work each new connection requires.
Why does MCP matter for AI software?
It reduces the integration work needed to give an AI application access to real data and tools, which is often what separates a genuinely useful AI assistant from one limited to whatever a user manually types or uploads into it.