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ElementX

MCP servers

Connect AI agents to your systems, on your terms

An MCP server is the controlled connection between AI agents and your systems. Your teams' assistants, and your customers' agents, work with live data and see only what each user is authorised to see.

Talk to us Read the risks and controls whitepaper

Your AI assistants are only as useful as the systems they can reach

The Model Context Protocol (MCP) is an open standard for connecting AI agents to software and data. Out of the box, most assistants work with documents and email, while the systems your organisation runs on stay out of reach: customer accounts, claims, student records, asset registers and case files. An MCP server opens those systems up with the controls your risk team expects.

An MCP server builds on your APIs to give AI agents one standard connection

Your APIs are built for developers, who write and maintain code for every integration. An MCP server is built for AI agents: it describes a focused set of tools in plain language, and any assistant that supports MCP can connect without new integration code for each one. It works through your existing APIs and data sources, so your APIs stay in place.

Without an MCP server, agents find their own way into your systems

Staff paste exports into AI tools; customers and vendors build their own connectors or point agents at your interfaces with browser automation. Each route runs on terms you didn't set. The cost is more load on your systems, answers that drift from the record, and agents holding full logins or broad API keys with no audit trail.

Built around your tasks, your permissions and your peak loads

  • Designed around the jobs your people do

    We start from the tasks people want agents to complete and design a focused set of tools for them. Exposing every system function gives agents too many choices and worse answers.

  • Exposes only what you mean to expose

    Authentication, permission scoping and audit logging are built in, and your privacy and risk teams can trace every call.

  • Protects the systems behind it

    Rate limits, query guards and caching stop agent traffic from overloading your core systems, including legacy ones never built for this kind of demand. Load testing shows how the server behaves before go-live.

Built on the controls behind enterprise AI already in production

Mott MacDonald's knowledge agent scopes every answer to what each employee is authorised to access, across a workforce of around 20,000. The University of Auckland's student assistant has run in production since 2020 and handles the spikes around exams and enrolment. Each MCP server we build carries the same discipline.

Talk to us about an MCP server

Organisations we've worked with

  • University of Auckland
  • Mott MacDonald
  • Southern Cross
  • New Zealand Defence Force
  • The Warehouse Group
  • Deutsche Telekom

From the first conversation to a server your team owns

  1. Design the tools around real use

    We work with the people who will use it, and with your security and risk teams, to agree the data each tool should reach and what stays out of bounds.

  2. Build it secure and ready for load

    Security controls go in from the first sprint, and the server runs in your environment, including onshore New Zealand hosting where your data needs to stay. We test it against the AI assistants your teams use.

  3. Launch, monitor and hand over

    The server goes live with monitoring in place and documentation for the developers who connect to it. Start with your own teams, then open the same server to customers and partners when you're ready. Your team owns the code and knows how to extend it.

Ready to give AI agents a controlled way into your systems?