Introducing MCPCloud

MCPCloud turns an API specification into a deployed MCP server. Paste an OpenAPI, Swagger, or GraphQL spec; the platform extracts every operation as a typed tool, rewrites each tool description so an agent can actually use it, generates a TypeScript MCP server, and deploys it to a global edge runtime. The whole loop — import, enrich, generate, deploy — takes minutes.

The problem, plainly

MCP is becoming the standard way to connect AI agents to real systems, but building a good MCP server is still mostly manual work. Raw API specs make bad tool catalogs: descriptions written for human developers reading reference docs are close to useless for a model choosing between forty tools mid-conversation. And an endpoint-per-tool dump of a large API blows out the context window before the agent has done anything useful.

The hard part was never the protocol plumbing. It is curation and description quality — deciding which operations belong in an agent’s hands, and explaining each one well enough that the model picks the right tool with the right arguments on the first try. That is the part MCPCloud is built around.

What the pipeline actually does

• Import — paste an OpenAPI / Swagger / GraphQL spec, or upload a manual definition. Every operation is extracted with its parameters, schemas, and auth requirements.
• Enrich — AI rewrites every tool description for agent comprehension: what the tool does, when to reach for it, and what each argument means in practice.
• Generate — a typed TypeScript MCP server. Real source code you can read, not an opaque runtime wrapper.
• Deploy — one click to a global edge runtime, fronted by a policy proxy that handles auth, rate limits, runtime key injection, and observability.

None of these steps is exotic on its own. The point is that they are one pass: the spec you paste is the server your agent connects to, and everything in between is inspectable along the way.

The platform after the deploy

The pipeline is the on-ramp, not the product. What makes MCPCloud a platform rather than a generator is what happens after the deploy: every server runs behind a policy-enforcement layer that terminates auth (API key, OAuth 2.0, or custom headers), applies rate limits, and injects secrets at runtime — encrypted, never in the generated source. Static analysis gates every build before it ships. Per-tool usage, latency, and error rates land in the dashboard, and every release is an immutable version your clients can pin.

Distribution lives in the same place. Publish a release to your private org registry or the public marketplace and the platform handles versioning, discovery, and installs — the server you built this morning is the server your team, or anyone, can install this afternoon. One home for the whole lifecycle: built here, secured here, observed here, distributed here.

Proof: the Stripe showcase

We tuned the pipeline for agents, not codegen theater — so we ran it against one of the largest API surfaces around. We ingested Stripe’s full OpenAPI spec: roughly 470 operations parsed, then curated down to 34 tools that cover the workflows an agent actually performs — customers, payments, invoices, subscriptions — instead of mirroring every endpoint Stripe has ever shipped.

The result is a live MCP server running against Stripe in test mode. Call its tools from the sandbox on our landing page, or point your own MCP client at it, right now. The numbers above aren’t projections — they are what the pipeline produced.

Skill Studio

Servers give agents capabilities; skills encode how those capabilities should be used. Skill Studio lets you author composable, versioned AI workflow packages — a visual DAG with a dependency graph, guardrails, and sandboxed test runs — and publish the result as a SKILL.md artifact through a private org registry or the public marketplace. If an MCP server is the ingredient list, a skill is the recipe your team has agreed on.

No lock-in, on purpose

Every server ships its generated TypeScript source — real code, and it is yours. The self-host export packages that code with a Dockerfile and docs so you can run it on your own infrastructure. If you leave, you leave with a working server. We would rather earn the hosting than hold the code hostage.

Where to start

MCPCloud is in open beta. Try the live Stripe showcase first — it is the fastest way to see what an enriched, curated MCP server feels like from the client side. Then paste your own spec: the free tier goes all the way from import to a live deployment. This is a first release, and there is plenty we still want to build — but the core loop works today, end to end, on a real API.