Service
MCP server development
The Model Context Protocol is an open standard for exposing tools and data to AI applications. Practically, an MCP server is the difference between your team pasting exported spreadsheets into a chat window and their assistant querying the real system directly, with your permissions and your audit trail intact. It is the integration layer that makes everything else on this site possible.
You might recognise
- Staff paste internal data into chat tools because it is the only way to use it.
- Every AI tool needs its own bespoke integration with the same systems.
- You want AI access to internal data without loosening who can see what.
How it runs
Four steps, in this order.
- 01
Choose the surface
Which operations to expose as tools, and just as importantly which to leave out. A narrow, well-named tool surface produces better agent behaviour than exposing an entire API.
- 02
Design the permission model
The server acts on behalf of a user, so it inherits that user permissions rather than holding a superuser token. Authorisation is designed before the first tool is implemented.
- 03
Build and document the tools
Each tool with a description precise enough that a model calls it correctly without trial and error. Tool descriptions are part of the interface and are written with the same care as the code.
- 04
Test against real clients
Verified against the MCP clients your team actually uses, because protocol compliance and working in practice are not the same thing.
What you get
Deliverables
- A deployed MCP server exposing the agreed tool surface
- A permission model that inherits the calling user access, not a shared admin token
- Tool documentation written for models and for humans
- Verification against the MCP clients your team uses
- Source and deployment configuration, owned by you
FAQ
About mcp development
What is an MCP server, briefly?
A small service that exposes tools and data to AI applications over a standard protocol, so one integration works with any client that speaks it rather than needing a bespoke plugin per tool. There is a longer explanation in our guide to MCP servers.
Does this mean giving an AI model access to our database?
It means giving it access to specific, named operations that you define, under the permissions of the person calling. That is a considerably narrower surface than a database connection, and it is auditable in a way that pasting exports into a chat window is not.
Which MCP clients will it work with?
Any client implementing the protocol, which includes Claude and a growing set of editors and agent frameworks. We verify against the specific clients your team uses, since real-world behaviour varies.
Can you connect systems that have no public API?
Often, but it depends on what the system exposes. Assessing that honestly is part of the first step, and if the only route in is fragile screen-scraping we will tell you that rather than build on it.
Related reading
- What is an MCP server?A plain-English explanation of MCP servers: what they do, the three things they expose, how they connect to AI apps, and when you actually need one.
- Agentic AI vs AI agents: what the difference actually meansThe difference between agentic AI and AI agents, why it matters when you are buying, and the questions that reveal which one you are being sold.
Tell us what the work looks like
Describe the process you want off your team plate. You will get a straight answer on whether agents are the right tool for it, including when they are not.