adobe-custom
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@adobe-customsearch for content fragments about marketing"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Adobe Custom MCP with Frontend
This repo gives you two interfaces over the same Adobe tool layer:
Claude / MCP interface
local MCP server name:
adobe-customused by Claude Code in VS Code
Human-friendly web interface
browser chat UI
backend API
same underlying tool handlers
Why this architecture
The frontend does not call MCP over JSON-RPC by default. Instead, the frontend backend and the MCP server both import the same shared tool registry.
That gives you:
one source of truth for tools
easier debugging
cleaner auth/config handling
the option to add a real LLM later
Related MCP server: MCP360 Universal Gateway
Start
Install
npm install
cp .env.example .env
npm run checkStart the web UI
npm run start:apiOpen:
Start the MCP for Claude
In a second terminal:
npm run start:mcpThen in Claude Code:
/mcp
Use adobe-custom health_checkCurrent frontend chat behavior
The browser UI supports:
health check
fragment search
fragment fetch by ID
AEP sandboxes
AEP datasets
AEM content fragment fetch by path
The chat endpoint currently uses a heuristic intent router so it works without external AI keys. You can later replace that with a real LLM planner while still reusing the same tool registry.
Key folders
apps/web/ # frontend UI
services/api/ # backend API and chat router
src/ # MCP server + shared tool implementationsNatural-language UI responses
The web UI now summarizes tool output in natural language and keeps the raw tool output inside an expandable details section.
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