An MCP server exposes your data and actions to AI assistants through the Model Context Protocol, an open standard. Instead of building a bespoke integration for every AI tool you adopt, you build one server and any MCP-compatible assistant can use it, within permissions you define.
One interface instead of N integrations
Every AI tool a company adopts wants access to the same things: the CRM, the document store, the ticketing system, the database. Built ad hoc, that becomes a tangle of one-off integrations with inconsistent permissions and no audit trail — and it has to be redone when you switch assistants.
MCP standardises the interface. You define once what can be read, what can be acted on, and by whom. New assistants connect to the same server. Access is governed in one place rather than scattered across vendor configurations.
Process
How we deliver it
Process flow for MCP server development
01
Inventory the systems
What data and actions should AI tools reach, and what must stay closed.
02
Design the surface
Define tools, resources and prompts with least-privilege defaults.
03
Build the server
Implement against your systems, with authentication and rate limits.
04
Secure and audit
Permission mapping, logging of every call, and abuse protection.
05
Connect clients
Wire up your assistants and document how future ones connect.
Deliverables
What you receive
A running MCP server with documented tools and resources
Authentication, permission mapping and rate limiting
Full audit logging of every access and action
Client configuration for your current AI tools
Developer documentation for extending the server
Engagement shape
Four to eight weeks depending on how many systems are exposed. We usually start with two systems and extend once the pattern is proven.
Stack decisions follow the problem. This is where we usually start, not a fixed menu.
Frequently asked
Questions we get about this
Is MCP stable enough to build on?
It is an open standard with growing adoption across major AI tools, which is precisely why it is worth building against rather than another vendor-specific connector. That said, it is young — we build the server so the underlying integration layer is separable, meaning a protocol change costs an adapter rewrite rather than a rebuild.
Can we limit what AI tools are allowed to do?
That is the main reason to use MCP rather than direct API keys. Every tool is explicitly declared, permissions are least-privilege by default, destructive actions can require confirmation, and every call is logged against an identity.
Talk it through before you commit
A discovery call is a working session on your constraint, not a sales pitch.
