MCP server development to make your product usable by AI agents

Make your product usable from AI assistants

Many of your customers already work inside an AI assistant. They ask Claude, ChatGPT or Cursor to look things up, draft, plan and update records. If your product can't be reached from there, they copy and paste between windows, or they pick a tool that can be reached. An MCP server fixes that: a small, well-defined layer that lets an assistant call your product's tools with the user's own permissions.

This service is for SaaS founders, product leads and CTOs who want their app to work in that setting, and for teams who want AI assistants to use internal systems safely. If you want AI features inside your own interface instead, see AI integration.

What a good MCP server needs

A demo server that answers one call is quick to build. One your customers can rely on takes more care:

• Tools shaped around tasks, not around your database tables. "Find my open bookings for next week" works better than five generic endpoints, because the assistant picks the right tool more often.
• Clear names and descriptions, since they are what the model reads when it decides what to call.
• Real authentication. Each user connects their own account through OAuth and only sees their own data.
• Guardrails on write actions: permission checks, confirmation for anything destructive, rate limits and an audit log.
• Testing in real clients, because each assistant handles tool calls slightly differently.

Proof

I built the MCP server for WithMaslow, a personality assessment platform, with Laravel MCP on top of the Laravel 13 API I rebuilt for the product. AI clients can use the product through it, alongside the AI-generated insights that run inside the app.

I work in both Laravel and TypeScript, so the server can live inside your existing back end instead of becoming a separate system to maintain. For a Laravel product, this pairs naturally with Laravel development.

What affects the price

MCP server projects start from €1,900; you get a fixed quote after a short scoping call. The main factors:

• the number of tools, and whether they only read data or also change it
• whether there is an existing API to build on
• internal use by your own team versus OAuth for every customer
• how many AI clients must be tested and documented

Being usable by AI agents goes beyond MCP. If you also want your public pages read and cited by AI assistants, technical SEO and performance covers structured data and llms.txt.

Start with a short call

Send me the tasks you'd like your customers to hand to an AI assistant. I reply within one working day, and after a short call you get a written proposal with a fixed price. Get a quote for an MCP server.