Services / MCP Development
Give your AI working hands.
Custom Model Context Protocol servers that let Claude, ChatGPT, Cursor, and Copilot use your real systems — with boundaries you define.
An assistant that can only talk is half a tool
Every company that adopted AI assistants hit the same wall: the assistant is articulate and completely ignorant of the business. It cannot see the order, the ticket, the customer record, or the internal rule that determines the right answer. So people paste context in by hand, which caps the value at whatever someone is willing to copy.
The Model Context Protocol fixes that properly. It is an open standard — introduced by Anthropic and now supported across the major assistants — for exposing tools and data to an AI client. Build one server, and Claude, ChatGPT, Cursor, and Copilot can all use it, rather than building a separate integration per vendor and rebuilding them all next year.
How a custom server is built
Choose a small, sharp tool surface
Not a mirror of your whole API. A handful of tools with unambiguous names, clear descriptions, and predictable arguments — because assistants pick tools from descriptions, and overlapping tools produce unreliable behavior.
Enforce permissions at the server
Scoped credentials, allow-listed operations, read-only by default with writes added deliberately, and per-user identity rather than one shared service account. The model never gets more reach than the server grants.
Treat returned content as data
Records coming back from your systems are never interpreted as instructions. Without that rule, a malicious value in a database row can steer an assistant into doing something nobody authorized.
Log, test, and version it
Every tool call audited, a test suite that runs against the real contract, and versioning so the tool surface can evolve without silently breaking the assistants already relying on it.
A server you can install right now
Rather than describing capability in the abstract: autoMessage’s MCP server is published publicly on npm. Ten tools, two transports (stdio and streamable HTTP), running against a live production API. It installs in one command.
claude mcp add automessage --scope user -- \
env AUTOMESSAGE_API_KEY=<key> \
npx -y @stockmandigital/automessage-mcpMCP, answered
What is an MCP server?
A Model Context Protocol (MCP) server is a standard interface that lets AI assistants use external systems. Instead of an assistant that can only talk about your data, an MCP server gives it a defined set of tools it can call — read a record, search a database, send a message — with the permissions and boundaries you specify. MCP was introduced by Anthropic as an open protocol and is now supported across Claude, ChatGPT, Cursor, Copilot, and other clients, which means one server works with many assistants instead of one integration per vendor.
Why would a business build its own MCP server?
Because the valuable context is internal. Public assistants know nothing about your inventory, your customers, your ticket history, or your internal rules. A custom MCP server exposes exactly those systems to an assistant in a controlled way, so employees can ask questions and take actions against real company data instead of copying information into a chat window.
How is MCP different from just giving an AI our API?
An MCP server is a deliberate translation layer, not a raw pipe. APIs are designed for developers who read documentation; assistants need tools with clear descriptions, predictable arguments, and sensible defaults. The server also enforces what an assistant is allowed to do — which endpoints, which records, which write operations — independently of what the underlying API technically permits.
Is it safe to connect AI assistants to internal systems?
It is as safe as the boundaries you build. The server, not the model, decides what is reachable: scoped credentials, allow-listed operations, read-only by default with writes added deliberately, per-user identity rather than one shared service account, and audit logging of every call. The critical design rule is that content returned from your systems is treated as data, never as instructions — otherwise a malicious record could steer the assistant.
What does a custom MCP server typically expose?
Usually a small, well-chosen set of tools rather than an exhaustive mirror of an API. Common patterns are search over internal documents and records, reads of a specific entity such as a customer or order, a scoped write such as creating a ticket or updating a status, and triggering an existing internal workflow. Fewer, clearer tools produce more reliable assistant behavior than many overlapping ones.
How long does building one take?
A focused server covering a handful of tools against an existing API is usually a two-to-four week build including authentication, tests, and deployment. The variable is rarely the protocol — it is how clean the underlying systems are and how quickly access and credentials can be arranged.
What proof is there that you have actually done this?
A working MCP server published publicly on npm as @stockmandigital/automessage-mcp. It exposes ten tools over two transports against a live production API, and it is installable today in Claude Code, Claude Desktop, Cursor, and any other MCP client.
Make your systems usable by AI
Tell me which internal system you want an assistant to reach and what it should be allowed to do. I will tell you what the tool surface should look like and what building it takes.