MCP Server Development
Give AI agents controlled access to your systems, with permissions and a log.
The Model Context Protocol (MCP) is an open standard for connecting AI applications to tools and data. An MCP server is the controlled doorway: it decides what an AI client can read and what it can do in your systems, and it is the right place to enforce permissions and keep a record.
Most businesses do not need one yet. A fixed workflow, a Make or Zapier scenario, or ordinary tool use inside your own code is simpler. A server earns its place when several AI clients or agents need one governed point of access to the same systems.
We build MCP servers on the same footing as any production integration: the server calls your existing business logic rather than going around it, so permissions, approval gates and the audit log still apply. We start read-only, add proposals next, and allow writes only where a person approves and the change can be undone.
What you end up with
One governed doorway
AI clients reach your systems through a single server with its own authentication and per-user permissions, instead of each agent holding its own keys.
Read-only first
Agents start by looking, then by proposing, and only then by acting, with a person approving every step that cannot be undone.
Your rules still apply
Tools call the same business logic as your application, so validation, approval gates and the audit trail are not bypassed by an AI client.
Narrow by design
A small set of well-named tools with tight inputs and bounded output, rather than a general-purpose door into the database.
What actually lands
Shaped to the engagement, but this is the shelf it comes off.
- 01An MCP server over the systems and data you choose, with documented tools
- 02Authentication and per-user permissions mapped to your existing roles
- 03Read-only tools first, then approved, reversible write tools
- 04Audit logging of every call, with replay for investigation
- 05Limits on output size, rate and spend
- 06Hostile-input testing and a short operating guide for your team
Where this has shipped
MCP servers, specifically
A program that exposes tools and data to AI applications through the Model Context Protocol, an open standard. The AI client asks the server what it can do, and the server decides what is allowed. It is where permissions and logging belong.
Often not yet. If the work is a fixed workflow, a scenario in Make or Zapier, or a single application calling a model, that is simpler. A server makes sense when several AI clients or agents need controlled access to the same systems. We will tell you if you do not need one.
It is as safe as the controls around it: least-privilege tools, read-only by default, approval on anything irreversible, limits on output and spend, logging, and testing with hostile input. Content an agent reads can contain instructions, so we treat anything the agent reads as untrusted.
One build, inside the solar operating system case study, exposes the same tool registry the in-app copilot uses over a standard MCP interface. We have no standalone MCP server case study published, and we say so rather than imply more.
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Tell us about the process that keeps costing you.
A 15-minute call is usually enough to say whether this is an automation, an integration, or something that should be built, and whether we are the right people for it.
What happens on the call
- 15 minutes, no deck
- We map the process on the call
- A written scope, or an honest no