Why would my SaaS want one?

So AI assistants can answer about your product from a customer's real account and take real actions, instead of guessing from general knowledge.

What do you need from us to start?

The data and actions you want exposed, and how access should be scoped. Then it is a scoped build with the method documented.

How is access secured?

Access is scoped deliberately - the server exposes only what you define, with governance over what an assistant can read and do.

Which assistants does it work with?

Any assistant that supports the Model Context Protocol - it is an open standard. Claude and ChatGPT both do, and the list is growing.

MCP Server Development for SaaS

Yellow House Digital builds MCP servers - the Model Context Protocol layer that lets AI assistants use your product's data and actions safely. Built for SaaS teams, with the integration methods published.

What an MCP server does

The Model Context Protocol is how an AI assistant reaches outside its own context to use a real system - your product's data, and the actions it can take. An MCP server is the layer that exposes those capabilities to assistants like Claude and ChatGPT in a controlled, described way.

For a SaaS team, it is the difference between a customer asking an assistant about your product and getting a guess, versus getting an answer grounded in the customer's actual account.

Every MCP server stands on an API - the endpoints that actually hold the data and perform the actions. I build both: the API underneath and the MCP layer on top, so they fit together instead of being stitched across two vendors.

How a build runs

Five steps, in order.

01Scope - the data and actions an assistant should reach, and how access is bounded. This is the description we need from you.
02The API underneath - the endpoints that hold the data and perform the actions. Built or extended first, because the MCP layer stands on it.
03The MCP layer - each capability exposed as a described tool, so assistants use it correctly instead of guessing.
04Governance - what an assistant can read, what it can do, and what it can never touch. Scoped deliberately, not by default.
05Handover - the integration approach documented, so your team can maintain it without me.

Built with continuity in mind

The harder part of these systems is not exposing data - it is doing so safely, and keeping the assistant coherent over time. Yellow House Digital has published work on exactly this: papers on persistent, governed memory for AI systems, and a full build playbook for the production MCP servers already running.

That means an MCP build here starts from how the system stays trustworthy across sessions, not just whether it returns data. For the non-technical version - what MCP is and why it matters - see MCP for Business Owners.

AI assistant

Your MCP servergovernance

Your product's data and actions

What we need from you

A description of the data and actions you want an assistant to reach, and how access should be scoped. From there the work is a scoped build with the integration approach documented, so your team can maintain it.

If you are not sure what should be exposed, that is a normal starting point - scoping it together is the first step, not a prerequisite.

Questions

The Model Context Protocol layer that lets AI assistants use your product's data and actions safely, in a described and controlled way.

Last updated: 15 July 2026

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