Category: Mcpfy AI

Stainless got acquired by Anthropic AI, so if you’re weighing Mcpfy vs Stainless for your next MCP server, here’s a direct comparison.

Below, we break down Mcpfy vs Stainless across everything from hosting to discovery, so you know exactly where each one stands.

What Stainless Does

Stainless is primarily an SDK generation company. It generates SDKs in TypeScript, Python, Go, Java, and more. On top of that, it added MCP server generation as one extra feature. In practice, this means it turns an OpenAPI spec into a TypeScript MCP server, which then ships as a subpackage of your Stainless SDK.

Mcpfy vs Stainless: What mcpfy.ai Does Differently

mcpfy.ai is built as a full platform from the ground up. It covers building, testing across every AI client, deploying, and monitoring, and it also helps you get your MCP server (or MCP app) listed in app stores. All of this happens in one place, and it works across ChatGPT, Claude, Gemini, Cursor, and custom agents.

Because Stainless is now part of Anthropic, teams looking for an independent option for their MCP infrastructure often land on mcpfy.ai as one of the strongest choices on the market.

Here’s what mcpfy gives you that Stainless doesn’t.

1. Turn an Existing API Into an MCP Server

Stainless can only generate a server from an OpenAPI spec, and it only outputs TypeScript. mcpfy, on the other hand, lets you hand over your API directly and get a working MCP server back, with no rewrite needed.

2. Build in the Language You Actually Use

mcpfy lets you build from an SDK, a coding-agent skill, or an API import, in whichever language you actually use. Stainless’s MCP server generation, meanwhile, works in TypeScript only.

3. Get It Hosted, Not Just Generated

Stainless doesn’t host your server for you. Once it’s generated, deploying it, whether as a Docker image, on Cloudflare Workers, or elsewhere, is on you. With mcpfy’s built-in hosting, however, you just connect a repo, and every push deploys automatically, complete with a live preview URL for every branch.

4. Know It Actually Works Across Clients

Stainless has no built-in way to test across AI clients. mcpfy’s Cloud Inspector, by comparison, runs the same tool call across GPT, Claude, and Gemini, and flags regressions automatically.

5. See What’s Happening Once It’s Live

Stainless focuses on generating code, not on watching the server once it’s live. mcpfy, in contrast, keeps full observability on your MCP server: tool-call analytics, latency and error tracking, and session replay, so you can see exactly how it’s being used and where it breaks.

6. Actually Get Discovered

Stainless doesn’t offer a path to listing or discovery. mcpfy, however, generates a submission pack and helps you get listed in the ChatGPT app store and the Claude connector directory.

7. Build Interactive MCP Apps, Not Just Tool Calls

mcpfy also has built-in support for interactive widgets inside MCP apps, the kind that render a chart or a UI right inside a chat. This isn’t part of what Stainless generates.

The Bottom Line on Mcpfy vs Stainless

Stainless is a solid way to turn a TypeScript SDK into an MCP server. That’s often where the road ends, though. mcpfy, by comparison, is built to take you the rest of the way: from that first server to something hosted, tested across every client, watched in production, and actually discoverable by the people who’d use it.

Start deploying on mcpfy.ai.

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 on alok@mcpfy.ai