MCP server development: give AI secure access to your tools
We build custom MCP servers that let AI models work with your existing systems, with the permissions and logging a production system needs. We maintain an open-source MCP server ourselves.
MCP (Model Context Protocol) servers are the bridge between AI models like Claude and your existing business tools. A server describes what a system can do as a set of tools, and any MCP-capable assistant or agent can then use them.
The protocol is the easy part. The work is deciding which actions to expose, who is allowed to call them, and what happens when a model asks for something it should not get. We build MCP servers as stage two of our AI deployment program, and we publish one of our own under MIT.
Scope
What we build
Servers for your business systems
Custom MCP servers that give AI direct, secure access to your CRM, ERP, databases, internal APIs and any other system in your stack.
Tool design
A small set of well-named tools with clear inputs and outputs, shaped around the tasks people actually want done rather than a mirror of your API.
Authentication and permissions
Read and write tools are split so they can be granted separately. Credentials are scoped to the smallest account that works, and anything that spends money or changes live data requires an explicit confirmation.
Logging
Every write is logged with the identity of the calling agent and its arguments, so you can see exactly what the AI did.
Spec migration
The July 2026 MCP spec drops sessions and the initialize handshake. We move existing servers to it and treat the change as a production release, not a dependency bump.
Agents on top
Once the tools exist, we build the skills and agents that use them to complete multi-step workflows.
Process
How we work
- 01
Inventory the systems
We list the tools your teams use and the tasks that cross them, and pick the few integrations that remove the most manual work.
- 02
Design the tools before writing code
We agree the tool list, the permissions and the failure behavior with you in writing.
- 03
Test against real systems
We run the server against your actual environment and check accuracy, latency, edge cases and failure modes.
- 04
Deploy and document
You get the server running in your environment, documentation, and a runbook. After handover, you own it.
Proof
Where this comes from
FAQ
Questions we get about this.
- What is an MCP server?
- A small service that exposes one of your systems to AI models as a set of tools, using the open Model Context Protocol. Any assistant or agent that speaks MCP can then use those tools.
- Which systems can you connect?
- Anything with an API or a database: CRM, ERP, helpdesk, finance, document stores and internal services. If a system has no API, we look at what it can export.
- Is it safe to let a model act on our systems?
- It is as safe as the permissions you give it. We split read and write tools so they can be granted separately, scope credentials narrowly, log every write, and require explicit confirmation before anything that spends money or changes live data.
- Have you published any MCP work?
- Yes. We maintain the open-source Google Ad Manager MCP server, in Python under MIT. It manages campaigns, line items and creatives through natural language.
- Do you build on the new stateless spec?
- Yes. New servers start on the July 2026 spec, and we migrate existing ones. Our article on the stateless spec covers what breaks and what gets easier to host.
What we have written about MCP and integration.
Other services
Tell us where the project stands.
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