GetMCP

Turning an internal API into something Claude or ChatGPT can call used to mean a bespoke server, a maintainer to babysit it, and a quiet prayer that nothing broke overnight. GetMCP collapses that to an afternoon: point it at an OpenAPI spec or a cURL, pick which endpoints become tools, and you have a working Model Context Protocol server behind one URL.

What GetMCP actually does It hosts the plumbing between your API and any MCP-aware AI client, so you can ship an assistant-facing surface without running a server yourself. You import an API from OpenAPI, Swagger, or a Postman collection and each endpoint becomes a callable tool with a typed schema. You attach per-tool scopes and transforms, author prompt templates users can invoke in one tap, and run the whole thing through a built-in playground before any of it goes live. Once it's out, every production call — request, response, latency, and the client that made it — shows up in the dashboard. There is no per-call ceiling and no cap on how many servers or tools you run.

The features that matter - Tools — callable functions backed by your API, with JSON Schema arguments, typed responses, and per-tool scopes. - Prompts — pre-authored templates with parameters, so a customer runs a workflow from their AI client instead of copy-pasting instructions. - Playground and logs — run every tool with real inputs before you expose it, then watch every production call as it happens. - Analytics surface — expose queries and dashboards as tools so users ask a question in plain English and get real numbers back.

How it fits in your stack GetMCP is the frontend for AI clients that your product does not already have. If Zapier or n8n is where your existing users automate against your API, GetMCP is where Claude or ChatGPT do — same endpoints, different consumer. It sits alongside your existing REST or GraphQL API instead of replacing it, which matters, because it means you can pilot an assistant-facing surface without rerouting production traffic.

Who it's built for Anyone who already has an API and wants an AI-assistant frontend for it without hiring for it. The clearest fits are SaaS founders who want customers running reports from Claude, agencies running branded servers across client sites as a paid add-on, and internal ops teams wiring private APIs into a self-hosted team copilot. Indie devs and no-code builders get a way in that starts from a single cURL, not a Kubernetes cluster, and WordPress or WooCommerce owners can point it at their site so shoppers browse and buy from an AI assistant.

How you actually use it The path from account to first live tool is short enough to do between meetings. You import your API definition, pick the endpoints worth exposing, tighten scopes, run the playground until each tool behaves, and then hand the server URL to your users' AI client. Once it's live, the logs view is where you actually live — that is how you spot the tool called ten times a day versus the one nobody touches, and how you catch a broken schema before support gets a ticket.

The opinion worth stating: most teams do not need to build the MCP layer themselves. If your differentiation is the underlying product, not the server that fronts it, buying this back is the right trade.