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JTL provides a Model Context Protocol (MCP) server that gives AI tools direct access to the full developer documentation, so they can answer questions, generate code, and help you build Cloud Apps with accurate, up-to-date information.

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Connect to your AI Tool

Choose your AI coding tool below and follow the setup instructions.
Add the JTL docs MCP server with a single command:
claude mcp add --transport http jtl-docs https://developer.jtl-software.com/mcp
Restart Claude Code, then run /mcp. You’ll see jtl-docs · ✔ connected as part of the MCP servers.
You can also connect directly from any page in these docs. Click the contextual menu (top of any page) and select Connect to Cursor or Connect to VS Code to add the MCP server with one click.
AI Assistant Prompt
  Help me build a JTL Cloud App. Use the jtl-docs MCP server for any
  platform details. Based on my project, do the following: 1. Set up a
  Next.js app with an AppBridge provider scoped to iframe routes. 2.
  Add a setup page, an ERP page that fetches items from the Cloud ERP,
  and a standalone Hub page. 3. Generate a manifest.json with the
  required lifecycle hooks and capabilities. 4. Suggest relevant next
  steps based on what I'm building, such as adding a pane, handling
  webhooks, or submitting to the App Store.

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What the AI Can Do

Once connected, your AI tool can: Answer platform questions. Ask “How do I verify a session token from AppBridge?” and the AI will pull the exact implementation from the authentication docs. Generate manifest files. Describe what your app does and the AI will generate a complete manifest.json with the correct capabilities, scopes, and lifecycle URLs. Write API integration code. Ask “Write a GraphQL query to fetch the first 20 items sorted by SKU” and the AI will generate the query, headers, and fetch logic based on the actual API docs. Debug authentication issues. Paste an error message and the AI can reference the troubleshooting docs to suggest a fix. Scaffold entire apps. Ask the AI to create a Cloud App with a specific feature, and it will follow the architecture patterns from the docs (AppBridge provider, session verification, API client).

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Tips for Better Results

Be specific about what you’re building. “Help me build a JTL Cloud App” is vague. “Create a pane app that shows customer order history in the customer sidebar” gives the AI enough context to generate accurate code. Reference JTL concepts by name. Use terms like “AppBridge,” “manifest.json,” “session token,” “JTL-Wawi GraphQL API,” or “lifecycle hooks.” The AI will map these to the correct documentation pages. Ask follow-up questions. After the AI generates code, ask “Does this follow the recommended AppBridge initialization pattern?” The AI will check its answer against the docs. Combine with the quickstarts. Start with the From Template or From Scratch quickstart to get a running app, then use AI to extend it with features.

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Other Ways to Use the Docs with AI

Beyond the MCP server, there are a few more ways to feed JTL docs into AI tools: Copy as Markdown. On any page, press Cmd+C / Ctrl+C or select Copy from the contextual menu to copy the page content as Markdown. Paste it directly into ChatGPT, Claude, or any other AI tool. Claude and ChatGPT links. Select Open in Claude or Open in ChatGPT from the contextual menu on any page. This opens the AI tool with the page content pre-loaded. llms.txt. The docs site hosts an llms.txt file that AI crawlers use to index the documentation. This helps general-purpose AI tools like ChatGPT and Perplexity get accurate answers about JTL’s platform.

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What’s Next

Quickstart: From Template

Clone the sample app and get a running Cloud App in 15 minutes.

Quickstart: From Scratch

Build a Cloud App step by step with Next.js and TypeScript.

Architecture Overview

Understand how Cloud Apps are structured before you start building.

Using Platform APIs

Call the Cloud ERP REST and GraphQL APIs from your app.