MCP servers that connect AI to the systems you run
Build MCP servers that expose your internal tools and data to AI assistants safely — one integration surface instead of a bespoke connector per model.
the model sees only the tools you granted
- Custom MCP servers
- Scoped permissions
- Read and write boundaries
- Best when
- Your team or an agent needs to reach internal systems through a controlled, logged surface.
- Not a fit when
- A single one-way export would do the job.
- First thing you get
- One MCP server over one system, with scoped permissions and an audit log.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in 2024, that lets AI assistants connect to external tools and data through one common interface. You build an MCP server describing what your systems can do, and any MCP-capable assistant can use it. We build MCP servers with authentication, scoped permissions and audit logging.
The Model Context Protocol is a standard way for AI assistants to reach external tools and data. Its practical value is straightforward: you build one server describing what your systems can do, and any MCP-capable assistant can use it — rather than writing a fresh integration for every model and every vendor.
The engineering that matters is not the protocol, it is the boundary. What does the assistant get to see, which actions can it take, how is access scoped per user, and what gets logged. We build MCP servers where those answers are explicit.
What this includes
- 01
Custom MCP servers
Expose your internal APIs, databases, and services as well-described MCP tools.
- 02
Scoped permissions
Per-user and per-tool authorisation so the assistant inherits the caller's access, never more.
- 03
Read and write boundaries
Clear separation between tools that inspect and tools that change something, with approval on the latter.
- 04
Audit logging
Every tool invocation recorded with caller, arguments, and result for later review.
- 05
Legacy system adapters
Wrapping older systems that were never designed for programmatic access behind a clean tool interface.
- 06
Deployment and hosting
Running your MCP server in your own infrastructure with the availability you need.
Situations we see most often
Let your team query internal systems conversationally
Staff ask questions of operational data through an assistant they already use, with their own permissions applied.
Give agents a controlled tool surface
MCP becomes the single, auditable boundary through which every agent touches your systems.
Stop rebuilding the same integration
One server serves every MCP-capable client, instead of a separate connector per assistant and vendor.
Expose a product capability to AI clients
If your customers use AI assistants, shipping an MCP server makes your product reachable from inside them.
What to know before you start
Why use MCP instead of building a separate integration for each AI tool?
Without a standard, every assistant and every model needs its own connector to every system, and each one has to be maintained as the systems change. With MCP, you build one server that describes what a system can do, and any MCP-capable assistant, whether a desktop assistant, an IDE or your own agent, can use it.
That makes the integration an asset you own and control, rather than a set of vendor-specific plugins.
What can an MCP server expose?
Tools, which are actions an assistant can take, such as searching tickets or creating a draft order. Resources, which are data an assistant can read, such as documents or records. And prompts, reusable templates for common tasks. Each is explicitly declared with a description, so the assistant knows what it does and when to use it.
We expose only what a given role should use, and split read and write tools so write access can be granted separately.
How are permissions enforced in an MCP server?
The server acts as the calling user, not as an all-powerful service account. Authentication passes the user's identity through, and every tool checks that user's existing permissions in the underlying system, so the assistant can never see or do more than the person using it.
Write actions can require explicit confirmation, and every tool call is recorded in an audit log with the user, inputs and result.
Can MCP connect to older systems without APIs?
Yes. Where a system has no API, we build an adapter in front of it, reading from its database or export files, or driving a defined interface, and expose that through the MCP server. The legacy system stays untouched while becoming usable by assistants.
What is a good first MCP server?
One system your team asks about constantly, with read-only tools first: searching tickets, looking up an order, finding a document. That delivers value quickly with little risk, and shows how people actually use it before any write access is added.
Once the permission model and audit log have proven themselves, write tools follow, behind confirmation for anything consequential.
What drives the cost of an MCP integration?
MCP servers are small compared with the systems behind them. These factors decide how much work one is.
| Factor | Effect | Why it matters |
|---|---|---|
| Number of tools and resources | High | Each tool needs a clear description, input validation, permission checks and tests. |
| Systems behind the server | High | Systems without APIs need adapters; well-documented APIs are much quicker to wrap. |
| Permission model | Medium | Passing user identity through and enforcing per-user access is careful work, and essential. |
| Write actions | Medium | Tools that change data need confirmation flows, idempotency and stricter testing. |
| Hosting and deployment | Low | Local, internal or remotely hosted servers each need modest setup and monitoring. |
What a typical engagement looks like
- 011–3 weeks
Discover
Process map
- 022–4 weeks
Design
Interactive prototype
- 03two-week cycles
Build
Working software each cycle
- 04ongoing
Ship & support
Automated pipeline
What we build it with
- Protocol
- MCPJSON-RPCTypeScript SDK
- Security
- OAuthScoped tokensAudit logging
Questions we get asked
01What is MCP in plain terms?
An open standard that lets AI assistants discover and call external tools. You describe what your systems can do once, in a defined format, and any assistant that speaks the protocol can use it — rather than each one needing a custom integration.
02Is it safe to expose internal systems this way?
It is as safe as the boundary you build. Tools are explicitly declared rather than open-ended, access is scoped to the calling user's own permissions, write actions sit behind approval, and every invocation is logged.
03Do we need MCP if we already have REST APIs?
MCP sits above them. It adds the descriptions, discovery, and permission model an assistant needs to use your APIs correctly — which a plain REST spec does not provide on its own.
04Which assistants can use our MCP server?
Any client that supports the protocol, including Claude and a growing number of IDEs and agent frameworks, as well as agents we build for you.
05Where does the MCP server run?
Wherever your security model requires: on users' machines for local tools, or hosted inside your infrastructure with authentication for shared access.
06How long does a first MCP server take?
Usually a few weeks for one system with read-only tools, permissions and audit logging in place; most of the time goes into the system behind it, not the protocol.
Adjacent capabilities
Tell us what you are building.
Share the problem and we will come back with an approach, a rough shape of the work, and what it would take to deliver it.