Custom MCP server development
We build Model Context Protocol servers that expose your internal systems to AI agents and assistants as well-defined, permission-scoped tools. Your teams can connect Claude, ChatGPT or in-house agents to company data while security keeps control over who can do what.
What it is and when it fits.
The Model Context Protocol (MCP) is an open standard for connecting AI applications to tools and data. An MCP server wraps a system such as your ERP, document store or internal API, and exposes specific actions and resources with typed inputs. We build these servers as part of your enterprise AI infrastructure, with OAuth authentication, per-user scopes, rate limits and audit logging.
MCP servers are the right investment when several AI tools or teams need access to the same internal systems, or when you want to offer your own product to customers' agents. If only one application ever calls one API, a direct integration is simpler. We help you decide which systems deserve an MCP server and in what order.
What we build.
Internal system servers
MCP servers for ERP, CRM, data warehouse or in-house APIs, exposing a deliberate set of read and write tools.
Authentication and scopes
OAuth and SSO integration with per-user and per-tool scopes, so agents act with the rights of the person using them.
Audit and observability
Logs of every tool call with user, parameters and result, sent to your existing monitoring and security tooling.
Tool design and documentation
Clear tool names, descriptions and schemas that models use correctly, tested with evals across several clients.
Public MCP servers for products
Customer-facing MCP servers for SaaS platforms, so your users can work with your product from their own AI assistants.
Gateways and registries
A central gateway that manages which servers and tools are available to which teams, with policy enforcement in one place.
How it works.
- 01
Choose systems and use cases
We list the tasks agents should perform, map them to systems and decide which actions are read-only, which need approval and which stay out of scope.
- 02
Design the security model
We agree authentication, scopes, data exposure and logging with your security team before writing the server.
- 03
Build and test the tools
We implement the server, test tool calls against real clients and run evals to check models pick the right tool with the right parameters.
- 04
Deploy and maintain
We deploy to your infrastructure, connect monitoring and keep the server current as the protocol and your systems evolve.
Related work.
Built with.
All technologiesFurther reading.
Common questions.
The protocol supports OAuth-based authorisation, but security depends on how the server is built. We enforce least-privilege scopes, validate every input, keep write actions behind approval where needed and log every call for audit.
Any MCP-compatible client, including Claude, ChatGPT, popular IDEs and agents built with frameworks such as LangGraph or PydanticAI. That lets you build the integration once and use it across tools.
Yes. We deploy remote servers in your cloud account or data centre, including EU regions, or as local servers for developer tooling. Your data does not pass through our systems.
A server around one well-documented API is a modest piece of work; servers spanning several legacy systems with fine-grained permissions take longer. We agree the timeline once the tools and security model are defined.