Custom MCP Server Development
The Model Context Protocol is how AI clients like Claude reach your real data and tools instead of guessing. A well-built MCP server turns "the AI cannot see our system" into "the AI works inside it." We build production MCP servers that expose your data and actions safely - and we have one running in a live client platform.
What an MCP server unlocks
Without one, an AI assistant is stuck with whatever you paste into a chat box. With a custom MCP server, it can query your database, call your internal tools, and take scoped actions - with permissions you control.
That is the difference between a clever chatbot and AI that actually operates inside your product.
What we build
A production MCP server exposing the right resources and tools from your systems, with authentication, scoped permissions, and safe action boundaries. Connected to Claude or another MCP client, tested for the edge cases, and deployed in your environment - not a localhost demo.
Built into the product, not bolted on
We wire the server into your data model and product flows so the AI’s view of your system stays correct as the product changes, rather than drifting out of sync.
Proof
A production MCP server that let Claude work inside a live client platform - and beat its report engine.
On Dr. Todd Hall’s platform we integrated Claude through MCP so it could work directly with the organization’s survey and assessment data, generating interactive reports that outperformed the platform’s built-in engine.
Read the case studyStack
Frequently asked questions
- What is an MCP server, in plain terms?
- It is a standard connector that lets an AI client like Claude reach your data and tools - reading records, calling functions, taking scoped actions - instead of being limited to whatever text you paste into a chat.
- Which AI clients can connect to it?
- Any MCP-compatible client. Claude is the most common today, and the protocol is designed so the same server works across clients that adopt it.
- Is it safe to give an AI access to our systems?
- Yes, because access is scoped, not open. We build the server with authentication, per-action permissions, and hard boundaries on what the AI can read and do, so it works only inside the limits you set. You see exactly what it can touch before anything goes live.
- How do we start?
- With the Remote Team Readiness Audit, then a short discovery that maps which data and tools are worth exposing before any build.