MCP explained: what the Model Context Protocol means for business AI

The Model Context Protocol is an open standard for connecting AI systems to tools and data sources. Think of it as a common plug shape: instead of every vendor building bespoke integrations, tools expose an MCP interface and any compatible AI can use them.

Why it matters commercially

• Less lock-in. Integrations built against a standard travel better between systems than bespoke connectors.
• Faster coverage. A tool that speaks MCP becomes available to everything that speaks MCP, rather than waiting for one vendor to build it.
• Clearer permissions. Because access goes through a defined interface, it is easier to scope what a given agent may reach.

What it does not fix

MCP is plumbing. It does not decide what work gets done, whether the output is good, who approves it, or what it costs. A system with excellent integrations and no supervision is still an unsupervised system.

Questions worth asking

• Which of my tools can you reach today, and how is that access scoped?
• If I revoke access at the source, does it stop immediately?
• Can an agent reach a tool it has no task-level reason to touch?

On our side, credentials are scoped per agent and kept out of prompts unless a run explicitly needs them — the detail is here.

Keep reading

• Every agent platform we get compared to, side by side