MCP comes up a lot in AI conversations these days. The name sounds complex, but the idea is simple: a standard way to connect AI to your company's systems. This post explains what MCP is and why it matters for a business.
1. A one-line definition
MCP (Model Context Protocol) is a specification for how AI models access external tools and data. Like USB-C for devices, once something is connected to the standard, many AIs can use that tool the same way.
2. Without it vs. with it
| Without MCP | With MCP | |
|---|---|---|
| How it connects | Custom development for each AI | Build once, reuse across AIs |
| What it can do | Fuzzy boundaries set by developers | Clear per tool: "read only", "can write" |
| When you switch | Rebuild connections when the AI changes | Keep connections, swap only the AI |
3. How companies use it
- ERP lookups — AI answers "which customers have outstanding receivables this month" straight from the ERP
- Public data — AI queries government support programs or company information APIs when needed
- Internal documents — answers backed by search results from policies and manuals
- Getting work done — registering a draft quote in the system after a person approves it
When connecting via MCP, we keep each tool's abilities narrow — for example, sales data is "read only" and quote registration happens "only after a person approves." You're not opening a door to AI; you're giving it a specific counter to work through.
4. MCP is a tool, not the goal
Adopting MCP shouldn't become the goal itself. First decide "which work do we want to reduce," then connect only the systems that work needs. SOL LABS defines the customer's problem first and builds only the connections it requires with MCP.
5. Getting started
- Pick one task to hand to AI
- List the systems and data that task needs
- Define read and write scope for each system
- Start with one small connection and check the impact
If data can't leave your company, both the MCP connections and the AI model can sit on your own servers.
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