MCP Server Development
Governed connections between your AI systems and the data they need, built on the Model Context Protocol.
An MCP server is a standard interface that lets an AI system reach a data source or tool through a governed, auditable connection, using the Model Context Protocol. Instead of writing a bespoke integration for every model and every system, you expose each system once and any compatible AI client can use it within the permissions you set. AMT builds custom MCP servers for enterprise systems and is ISO 27001:2013 certified.
// where_we_stand
Where we stand, honestly
MCP is a young standard. Anyone claiming a decade of experience with it is claiming something impossible, and it is worth noticing who does.
What we can tell you is what we have built: governed integration layers between AI systems and enterprise data, under ISO 27001 practices, as part of the agent work described elsewhere on this site. The protocol is new. The discipline of exposing enterprise systems to software safely is not, and that is the part that decides whether an MCP layer is worth having.
// the_problem
Every AI integration built twice
Most enterprise AI work ends up rebuilding the same plumbing. A connector to the CRM for one assistant, another for the next, each with its own authentication, its own logging and its own security review. Three assistants in, you have three integration surfaces to maintain and nobody can say with confidence what any of them is allowed to touch.
MCP replaces that with one governed interface per system. Build it once, audit it once, and every AI client you adopt afterwards uses the same controlled path.
// what_we_build
What we build
- Custom MCP servers
- For your databases, CRMs, ERPs and internal APIs.
- Access governance
- Scoped permissions, so an agent reaches only what it may.
- Audit logging
- Every call recorded and traceable.
- Secure data pipelines
- Sensitive data reachable without being copied out.
- Client integration
- Connecting your AI assistants and agents to the servers.
// why_now
Why this matters now
Build the integration layer once and it survives the model you chose this year. Model providers change, pricing changes, better options appear. A governed protocol layer is the part of an AI architecture worth making durable, and it is the part that a security team can actually reason about.
Working out where to start?
Our agent candidate checklist covers the same authority and access questions an MCP layer has to answer: what may it read, what may it write, what must a human approve. One page.
// the_honest_section
Who this is not for
If you run one AI assistant against one system and have no plans to add more, a direct integration is simpler and cheaper. MCP pays back when there will be several of either.
If the systems you need to reach have no API and no route to one, the protocol does not solve that. The integration problem comes first.
// faqs
Frequently asked questions
What is an MCP server?
A server that exposes a data source or tool to AI systems through the Model Context Protocol, so any compatible AI client can use it under controlled, auditable permissions.
Why use MCP instead of a direct integration?
Because the second and third AI system you adopt will need the same access. One governed interface per system, built and audited once, beats a new bespoke connector and a new security review each time.
Is it secure?
The protocol is a means of access, not a security model on its own. What makes it safe is scoped permissions, per-request access control, complete audit logging and a review of what each server is allowed to expose. That design work is the job.
Which systems can be exposed through MCP?
Anything with an API or that can be given one: databases, CRMs, ERPs, ticketing systems, internal services. Systems with no programmable interface need that problem solved first.
Do we actually need MCP?
Not if you have one assistant and one system. Yes, if you expect several AI clients reaching several systems, because otherwise you will build and re-review the same plumbing repeatedly.
Talk to the architect, not an account manager
Thirty minutes with the engineer who would design the integration layer. Bring your systems list and your access model.
Not ready to talk? Read how we build AI agents that act in your systems. No form.