The Model Context Protocol (MCP) is an open standard that allows AI to connect with a company's actual tools and data.
It acts like a bridge between the AI’s brain and the systems you already use—your CRM, database, spreadsheets, or even Google Maps.
In the past, every AI integration was different and costly. With MCP, everything becomes simpler and standardized.
It ensures AI is no longer a black box that responds without knowing anything about your business.
With MCP, AI can consult your data in real time and use that information to give much more accurate answers.
For example, instead of responding “I have no information,” the AI could tell you how many sales you had last month or who your best customer is.
Because it democratizes access to advanced AI. Previously, only large companies could afford custom integrations.
Now, an SME can have AI connected to their business without spending a fortune.
It also reduces implementation and maintenance times. What used to take weeks can now be done in hours.
MCP has three key components:
The host: This is the AI (like ChatGPT, Claude, or your own custom agent).
The MCP client: It translates to the AI what needs to be requested or done.
The MCP server: It connects to the data source or service (like your CRM or database) and delivers what's needed.
This whole process happens through a “common language” that allows different systems to understand each other.
For example, if the AI needs to query data from Google Sheets, there’s no need to build an integration from scratch. You can use an existing MCP server for Google Sheets.
With MCP, connecting your AI to various tools is much simpler. You don’t need to code from scratch or build each integration individually.
Less development time means lower costs. Plus, you can reuse MCP servers created by the community.
AI responds based on your actual data. This eliminates the typical “hallucinations” of models that lack context.
You decide what the AI can see or do—for example, read-only access, specific fields, etc.
You can start with one integration (like your customer database) and add more as needed.
An SME connects its chatbot to the purchase history. When a customer asks about an issue, the AI knows what was bought, when, and any previous claims.
Result: Faster, more accurate service without needing to repeat information.
An online store links its AI to the inventory system. The AI can check stock availability, suggest alternative products, or notify when an item will be restocked.
Result: Fewer abandoned carts and a better customer experience.
A shipping company connects its AI to Google Maps and its package tracking system.
The AI can inform customers where their package is or suggest more efficient delivery routes to the team.
Result: Fewer support calls and more on-time deliveries.
A factory connects its AI to machine sensors and its maintenance system.
The AI detects patterns to predict failures, recommends maintenance, or alerts the team before major issues arise.
Result: Fewer unexpected downtimes and savings on repairs.
A private school links its AI to the student and class database.
The AI can answer questions about schedules, subjects, pending payments, or a student’s performance.
Result: Less administrative burden and improved communication with families.
A consultancy connects its AI to its CRM and billing system.
The AI can generate reports, answer internal questions, or automatically follow up on business proposals.
Result: More time for important tasks and less manual work.
At first, yes. You'll need someone who understands how to install or configure an MCP server and set up the connections.
But once it's set up, day-to-day use can be managed by anyone on your team.
Darwin AI works with MCP to turn your business into a smart and automated operation in under a week. Try a demo now!