If you’ve used ChatGPT, Claude, or any AI assistant recently, you’ve probably run into the same wall we did. These models are remarkably good at understanding what you want, but they can’t actually do much unless someone has wired them up to your tools and data. Ask an AI assistant to check your calendar, pull a customer record, or update a support ticket, and unless a developer has specifically connected it to those systems, it simply can’t.
That connection layer has a name now: MCP, short for Model Context Protocol. It’s an open standard that lets AI apps and assistants talk to real software (your CRM, your database, your internal APIs) through something called an MCP server. Think of it as a common plug that lets any AI model talk to any tool, instead of every company building a one-off, custom integration for every AI client it wants to support.
MCP is spreading fast. More than 24,700 MCP server repositories have already been created, and that number grew 55% last quarter alone. But building one is only the first step, and it’s turning out to be the easy part.
The Part Nobody Talks About: What Happens After You Build It
Writing a basic MCP server can take an afternoon with the right SDK. Getting it to actually work, reliably, in production, is a different story entirely.
Does it behave the same way in ChatGPT as it does in Claude? What happens when a tool call fails halfway through? How do you know something broke before your users find out for you? How do you host it securely, update it without downtime, and see what’s actually happening once real AI assistants start calling it?
These aren’t edge cases. They’re the default experience for almost every team we’ve talked to that has built an MCP server. Building an MCP server is easy. Deploying and scaling it reliably is the painful part.
What mcpfy.ai Does
mcpfy.ai is a one-stop platform to build, deploy, scale, and monitor MCP servers, so teams don’t have to solve that problem from scratch on their own.
One platform. Every relevant AI surface. Connect once, and mcpfy handles it across ChatGPT, Claude, Gemini, Cursor, and your own internal AI agents, instead of maintaining separate integrations for each one.
In practice, that means:
- Build from a template, an SDK, an existing API, or a plain-language description of what you want your server to do
- Deploy your source code into a secure, hosted MCP endpoint in minutes, not weeks
- Test how your server actually behaves across major AI clients before you ship, not after
- Distribute it once and reach AI assistants wherever your users already work
- Monitor tool usage, latency, failures, and real usage patterns once it’s live
We’ve open sourced the core toolkit, because we believe the ecosystem is healthier when the fundamentals are available to everyone building on MCP, not locked behind a paywall.
Why We’re Building This
We’re building mcpfy because we believe this problem is worth solving well, not around.
AI agents are quickly becoming the primary way people and businesses interact with software. But an agent is only as useful as the tools it can reach, and today, connecting an agent to real software still means every team rebuilding the same plumbing from scratch: hosting a server, testing it across every AI client, catching failures before users do, watching what happens once it’s live. That work gets repeated at nearly every company trying to make its product work with AI agents, instead of being solved once, properly, and reused by everyone.
We want mcpfy to be that layer: the infrastructure that sits between AI agents and the existing software they need to use, so that connecting your product to the AI ecosystem stops being a multi-week infrastructure project and becomes something any team can do in an afternoon.
We’re still early. We have pilot deployments running across voice agents, developer tools, HR tech, and events, and we’ve spent a lot of time talking to teams building MCP servers of their own to make sure we’re solving the actual problem, not the one that looks good on paper. That work continues every day.
Where This Is Going
AI assistants are only as useful as the tools they can reach. Our goal is simple: make connecting your product to the AI ecosystem, and keeping that connection reliable as it scales, the easy part, not the part that eats your team’s time for months.
This is the first post on our blog, and we’ll be sharing more as we build in the open, including what we’re learning from the teams already building on mcpfy. If you’re building or maintaining an MCP server and want to talk shop, or you’re curious what mcpfy could do for your product, we’d love to hear from you.