Every new technology wave eventually needs a standard way to connect things.
The web had HTTP. Integration had REST and later OpenAPI. And as AI moved from answering questions to using tools, it needed the same thing: a standard way for AI applications to connect to data and systems.
That is what the Model Context Protocol, or MCP, provides.
What MCP Is
MCP is an open protocol that standardizes how AI applications connect to external tools, data sources and services. Anthropic introduced it in late 2024, and adoption spread quickly across AI platforms and developer tools. In December 2025 it was donated to the newly formed Agentic AI Foundation under the Linux Foundation, giving it vendor-neutral governance.
Before MCP, every AI application needed custom integration code for every system it wanted to use. MCP turns that many-to-many problem into something much simpler.
How It Works
MCP has a few simple roles:
- Host: the AI application the user works with, such as an assistant, an IDE or an agent platform
- Client: the component inside the host that speaks MCP
- Server: a lightweight service that exposes a system's capabilities over MCP
An MCP server can expose:
- Tools: actions the AI can take, such as "create case" or "search orders"
- Resources: data the AI can read, such as documents or records
- Prompts: reusable prompt templates for common tasks
Build an MCP server for your CRM once, and any MCP-compatible AI application can use it.
Why Architects Should Care
If this sounds familiar, it should. MCP is integration architecture for AI.
The same lessons apply:
- Reuse: one well-built server serves many AI applications
- Abstraction: AI does not need to know how the underlying system works
- Governance: capabilities can be controlled in one place
In many organizations, the best MCP servers are thin layers on top of existing, well-governed APIs. If you invested in API-led connectivity, you are already in a strong position.
The Risks to Manage
MCP makes it easy to connect AI to powerful systems, which is exactly why it needs governance:
- Over-powerful tools: a server exposing "delete any record" to an agent is a risk
- Untrusted servers: connecting to unknown third-party servers can expose data or allow malicious instructions
- Prompt injection: content returned by tools can try to manipulate the AI
- Weak authentication: servers must authenticate and authorize properly
Practical Guidance
- Keep an approved catalog of MCP servers, just like an API catalog.
- Build servers on top of governed APIs, not direct database access.
- Expose narrow, purposeful tools rather than generic "run anything" tools.
- Require approval for sensitive or irreversible actions.
- Log every tool call for audit and troubleshooting.
Final Thought
MCP is becoming the connective tissue between AI and the enterprise.
Treat it with the same discipline you apply to APIs, and it can make your organization agent-ready far faster and far more safely.