Your coding assistant, now fluent in Loop

Every Loop integration starts the same way. Someone reads the docs, wires up an API key, builds test orders by hand, and pokes at endpoints to learn how the platform behaves in practice rather than on paper. It's necessary work, and it's slow.

The Loop Developer MCP changes the shape of that work. It connects the AI assistant you already use (Claude Code, Claude Desktop, Cursor or VS Code Copilot) to Loop, so the assistant can call Loop's APIs, read Loop's architecture guidance, and test against a real Loop shop while it writes your code.

• Every public Loop API operation, as more than 140 tools the assistant can call: returns, commerce data, webhooks, labels, grading and disposition, fraud lists, Shop Now and Checkout+.
• 32 reference documents on how Loop actually behaves: an architecture guide, integration patterns for warehouses, ERPs and labels, and a list of known traps.
• A sandbox to build against, with realistic test data on demand.

The shortest description is the one in the project itself: an API REPL plus Loop's architectural knowledge, wired into your editor.

Plain language in, real API calls out. The assistant works out which calls to make, runs them against your sandbox, and shows you what came back.

That webhook isn't a generic sample from the docs. It's built from your shop's own data, so the payload your code has to parse is the one it will actually receive.

Edge cases are where integrations break, and they're the hardest thing to set up by hand. The MCP seeds them on request: eleven named return scenarios, including a partially fulfilled order, a discounted order, a multi-currency order, a return outside the window and a final-sale item.

It works on both kinds of Loop shop. On a Loop Anywhere shop it creates the data through the Commerce Data APIs; on a Shopify shop it picks matching products from the catalogue. And if a scenario can't be built faithfully on your shop, it says so and skips it rather than faking it.

Loop Anywhere integrations get the full set: products, variants, collections, customers, locations, inventory and orders, pushed into Loop under your own IDs. Shopify-backed shops see the tools that apply to them.

Either way, the assistant is reading the same guidance Loop's solutions team uses to design integrations, so the code it writes starts from patterns that already hold up in production.

It runs on your machine, with your own Loop API key. Nothing is hosted.

• Missing permissions are caught before a call goes out.
• Every write is stamped with the shop it landed on.
• Seeded orders only use reserved test email addresses, so no shopper gets a stray confirmation.
• Credentials stay on your machine, and logs are redacted.

One rule matters: use a sandbox shop. The tools create returns, process refunds and issue store credit for real, and there's no dry-run mode.

Put the pieces together and the shape of a project changes. An implementation plan breaks an integration into four parts: pushing data to Loop, hearing about returns, managing them in flight, and executing the outcome. An assistant with the MCP can work through those as a to-do list, writing the code for each part and testing it against the sandbox as it goes.

The slow loop of read, guess, try and read again gets much tighter, and the questions that used to wait for a call with Loop get answered in the editor. We use it to stand up integrations ourselves, and it has changed how quickly we get from a design on paper to a working return.

It's early. The Loop Developer MCP is a reference implementation, provided as is, and it's still being refined. Sandbox shops are set up by Loop rather than self-serve. If you're building an integration with Loop and want to try it, ask your Loop solutions contact.

It's a different thing from the Loop MCP Server, which lets an AI assistant work with returns on a merchant's behalf. That one is for running returns. This one is for building integrations.