When someone asks us to build an MCP, I want to hear about the business task before the protocol. A customer checking whether a service fits, a staff member looking up an approved policy, and an operations team updating a record are different jobs. They need different data, permissions, and review.
We begin by mapping the current route: who asks, where the answer lives, who can provide it, and what happens next. That often exposes simpler work first. Perhaps service information is inconsistent across documents. Perhaps a team manually copies details between tools. If the process itself is unclear, connecting an AI to it will make the unclear part faster, not better.
Once the need is specific, we identify the smallest useful interface. An MCP server can expose tools or resources to a compatible client. Each tool should have a narrow purpose, understandable inputs, and output that the client can interpret. We decide whether access should be read-only, whether identity or account permissions apply, what information leaves the business, and where a person must approve an action.
For planning, work starts at USD $25/hour and can cost $40/hour or more. Maintenance is usually about $200/month. We confirm the hours and scope before quoting.
Keep the surrounding system in view
The MCP server is one part of a route that also includes the AI client, model provider, user, and source system. The data path matters. A hosted model may receive information returned by a tool; hosting the server yourself does not make that model local. We make those boundaries visible while scoping, rather than leaving them for a privacy notice at the end.
Then we implement against the actual system where practical and exercise the interface with ordinary and malformed requests. We check that a tool does not expose more fields than needed, that unavailable or ambiguous data is handled plainly, and that a failed downstream system does not look like success. For any consequential write, confirmation belongs in the flow. The exact configuration and display vary by AI client, so integration testing needs the intended client as well as the server.
Our public MCP explainer, Why a service-based business needs an MCP, explains the relationship between reusable instructions and live connections. Our implementation notes show why fitting a bounded server around an existing repository can preserve its established logic.
Make a useful first release
A first release might answer one class of question or prepare one item for a person to review. It need not expose every system. We document assumptions, ownership, and what to do when the connection is unavailable, then leave room to learn from actual use. A technically valid server is not a promise that a client will list it publicly or that customers will find it there.
Brownsmith Dynamics works across business efficiency, AI implementation, and automation. If you have a workflow worth examining, reach us on WhatsApp or through the contact page. We can map the task and existing software before deciding whether MCP belongs in the solution.