MCP Server Engineering

A managed MCP server in production needs more than the spec. It needs versioning, schema validation, observability, scoped authorisation, rate limits, and a deployment posture that respects data residency. We engineer those non-functionals because we have seen what happens without them: tool calls that succeed in dev and fail under load, audit logs that record nothing useful, and a single misconfigured tool that lets an agent see data it shouldn't.

Sovereign-residency considerations apply heavily here. An MCP server hosted in the wrong region defeats the entire point of in-region inference. We host MCP infrastructure inside the customer perimeter, with identity tokens that scope each tool invocation to the agent that called it.

The MCP security model deserves explicit design, not implicit assumptions. Every tool invocation carries an identity (which agent, on whose behalf, with what scope). Every tool surface declares the data classes it touches. Every call is logged with enough context to answer the regulator's question. We deliver this as a baseline because the alternative is an audit failure that closes the engagement.

Build versus buy on MCP servers is a real question now that the ecosystem has filled in. Off-the-shelf MCP servers exist for common SaaS surfaces (Slack, Google Workspace, Salesforce, Jira). We use those when they fit and recommend custom MCP development when the surface is bespoke (a private warehouse, a domain-specific workflow tool, a regulated data system). Most engagements end up with a mix, and we draw that line in the first design sprint.

Our pattern library covers the recurring shapes: read-only data adapters, write-capable workflow tools with approval gates, retrieval adapters with citation passthrough, and meta-tools that let agents discover and reflect on the tool surface. We bring those patterns to the engagement so we are not redesigning fundamentals every time.