MCP server development — make your product agent-ready.
Your customers are starting to work inside Claude, ChatGPT and their own AI agents. An MCP server — the open standard AI assistants use to connect to apps — lets them use your product from there, with access limited to exactly what you allow.
Julian Lopez · Founder & lead AI engineer · 3 years building AI for a 1M+ learner platform
Why teams come to us for MCP.
Customers ask for "an integration with Claude or ChatGPT."
Without an MCP server, every AI assistant needs its own custom integration — or your product gets left out.
Your API wasn't designed for AI agents.
Endpoints that make sense to developers confuse models: too many parameters, vague errors, huge responses.
Security is the blocker.
Handing an AI agent your API keys is a non-starter without per-user permissions and a log of what it did.
Work behind it.
MCP servers at a US EdTech
At a learning platform with 1M+ learners, Julian helped build custom MCP servers that exposed the platform's data to AI agents — the same pattern we now build for SaaS products.
AI support agent
Tool design in practice: an agent that searches a knowledge base and reads and creates tickets through typed, scoped tools.
Read the code →
What an MCP server can expose
Read your customers' data
Customers ask Claude about their own account — usage, invoices, orders, tickets — and get answers from your live data.
Take actions in your product
Create a ticket, update a record, start a workflow — with a confirmation step for anything risky.
Internal tools for your team
Your own team uses Claude or ChatGPT with your internal systems, instead of copy-pasting between tabs.
A typical first tool list
search_customers, get_invoice, list_usage, create_ticket — each typed, documented and limited to what the signed-in user can do.
Small, safe and documented.
Pick the tools
Which data and actions to expose, for whom, and what needs a confirmation.
Design for models
Clear names, typed inputs, small focused responses and errors a model can recover from.
Secure it
OAuth or scoped keys, per-user permissions, rate limits and a log of every call.
Document & ship
Setup docs your customers can follow in Claude, ChatGPT and other clients, plus tests and deployment.
Priced up front, in writing.
MCP server development
- Fixed scope, timeline and price before you commit
- Milestones with written acceptance criteria
- Code, docs and quality tests are yours
AI Production Audit
Already have a prototype? In five business days we measure quality, cost and latency, and hand you a prioritized, fixed-price plan.
See what's in the audit →Introductory founding-client rates, for up to three portfolio partnerships — in exchange for a case study if the work produces useful results.
Send a project brief
Tell us what your product does, what AI assistants should be able to read or do in it, and how your API works today. We reply within one business day.
Questions, answered.
Which AI assistants work with MCP?
A growing number of AI assistants and agent frameworks, including Claude and ChatGPT. One MCP server works across all of them.
Do we need to change our API?
Usually not. The MCP server sits in front of your existing API and turns it into tools that models use well.
How is access controlled?
Each user connects with their own credentials (OAuth where possible), so an assistant can only see and do what that user could in your app. Risky actions can require confirmation.
Where does it run?
In your infrastructure or as a hosted remote server — your call. We build in TypeScript with the official MCP SDK.
Tell us what you're building.
Book a free 30-minute call. You'll get a straight answer — and, if it's a fit, a concrete fixed-price plan. Sometimes the answer is "you don't need us."