How do I use ExD Accelerator MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@ExD Accelerator MCP Server Create a new offer from my CSV data for premium users" 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.

ExD Accelerator MCP Server

by khushi-nayal

Overview Schema Related Servers Score Discussions

JavaScript

Hybrid

ExD Accelerator — MCP Server

license node

AI-native Experience Decisioning lifecycle automation for Adobe Journey Optimizer. End-to-end ExD setup from a single chat conversation: CSV → schema fields → offers → collections → eligibility rules → ranking → selection strategy → placements.

21 MCP tools wrapping AEP Schema Registry and Decisioning APIs. Every write operation previews what it will do and requires explicit confirmed: true before executing.

Live endpoint: https://exd-mcp-server-without-auth.vercel.app/api/mcp Health: https://exd-mcp-server-without-auth.vercel.app/api/health

What's new in 2.0

Was in 1.x	Now in 2.0
Manual ACCESS_TOKEN paste, expires every 24h	Auto-mints via `client_credentials`, cached per client_id
Express + SSE local server, ngrok tunnel for sharing	Streamable HTTP serverless route — deploy to Vercel in one click
Single hardcoded sandbox in `.env`	Per-request config via HTTP headers — every coworker uses their own sandbox
README claimed 13 tools, code had 21	21 tools, documented
Bulk offers defaulted to `2024-06-10` (past date)	Defaults to today + 1 year
`lookup_decisioning_schema` showed `0 fieldgroups` on healthy schemas	Reads from `meta:extends` too — accurate counts
`.gitignore` had `".env"` (quoted) — would not actually ignore	Plain `.env`, plus `.vercel/`, log files, etc.

Related MCP server: IBM Decision Intelligence MCP Server

Two ways to run this

A. Local stdio (Claude Desktop)

npm install
cp .env.example .env       # fill in CLIENT_ID, CLIENT_SECRET, sandbox, schema, catalog
npm start                  # runs src/stdio.js

Then point Claude Desktop at it (%APPDATA%\Claude\claude_desktop_config.json on Windows, ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "exd-accelerator": {
      "command": "node",
      "args": ["/absolute/path/to/exd-mcp-server/src/stdio.js"],
      "env": {
        "CLIENT_ID":                 "…",
        "CLIENT_SECRET":             "…",
        "ORG_ID":                    "…@AdobeOrg",
        "SANDBOX_NAME":              "…",
        "TENANT_ID":                 "…",
        "DECISIONING_SCHEMA_URI":    "https://ns.adobe.com/…/schemas/…",
        "DECISIONING_SCHEMA_ALT_ID": "_….schemas.…",
        "ITEM_CATALOG_ID":           "xcore:decision-catalog:…"
      }
    }
  }
}

Restart Claude Desktop. You'll see the 🔧 tool icon — ExD Accelerator is live.

Push this repo to GitHub.
Import it in vercel.com/new. Framework preset: Other.
Deploy — Vercel auto-detects api/mcp.js as the serverless route.
Do not add Adobe credentials to Vercel environment variables. Each coworker supplies their own credentials via HTTP headers when they connect.

Adobe coworker setup

Once deployed at https://your-app.vercel.app, each coworker adds this in their MCP client config (Claude Desktop, Adobe AO Chat, Claude.ai, etc.):

Setting	Value
Server URL	`https://your-app.vercel.app/api/mcp`
Transport	Streamable HTTP
Auth	None at the transport level — credentials go in headers

Custom headers (one-time, in the MCP client config — NOT in chat messages):

x-adobe-client-id:      <Adobe Dev Console: Client ID>
x-adobe-client-secret:  <Adobe Dev Console: Client Secret>
x-adobe-org-id:         <IMS Org ID>@AdobeOrg
x-adobe-sandbox:        <sandbox name>
x-adobe-tenant-id:      <tenant id, e.g. acssandboxgdcthree>
x-adobe-schema-uri:     https://ns.adobe.com/<tenant>/schemas/<id>
x-adobe-schema-alt-id:  _<tenant>.schemas.<id>
x-adobe-catalog-id:     xcore:decision-catalog:<id>

The server uses client_credentials to mint a token automatically, caches it per client_id, and refreshes on expiry. The marketer never sees the token.

Is Vercel a good fit for a production MCP endpoint?

Yes, for this workload. Each MCP tool call is a single short HTTP roundtrip to Adobe Platform APIs — no long-running state, no streaming required, no WebSocket. Vercel's serverless model maps cleanly:

Concern	Verdict
Stateless requests	✅ Each MCP call is independent. No session state.
Cold-start latency	⚠️ ~300–500ms on first call after idle. Subsequent calls reuse the warm container.
60s function timeout (Pro tier)	⚠️ `bulk_create_offers` with >50 rows may exceed this. Chunk large batches.
10s timeout (Hobby tier)	⚠️ `get_setup_summary` is fine; large bulk is not. Upgrade to Pro for production use.
Auto-scaling	✅ Each coworker's request gets its own invocation.
HTTPS, custom domain, env-per-deploy	✅ Built in.
SSE / long-polling	❌ Not used here — we run Streamable HTTP with `enableJsonResponse: true`, which is single-request/response and fits serverless perfectly.

When Vercel isn't right: if you need server-initiated notifications, very large bulk operations (hundreds of writes), or stateful sessions across many calls, deploy to a long-lived host (Railway, Render, Fly, ECS) and run the same codebase. The transport layer is the only difference.

All 21 tools

Read-only (no confirmation needed)

#	Tool	What it does
1	`parse_csv_and_suggest`	Parses CSV, infers XDM types per column, suggests eligibility rules and ranking formulas. Always call first. No API calls.
9	`get_offer_item`	Fetches a single offer item by DPS ID
10	`list_offer_items`	Lists all offers in catalog with pagination
16	`get_setup_summary`	Full inventory: offers, collections, rules, formulas, strategies, placements
17	`lookup_decisioning_schema`	Full resolved schema with OOB + tenant fields; accepts `include_deprecated: true`
18	`list_schema_fieldgroups`	Lists all tenant fieldgroups for the offer item class
19	`get_fieldgroup`	Full field definitions inside a specific fieldgroup
20	`get_schema_audit_log`	Chronological change history for the decisioning schema
21	`list_schema_descriptors`	Identity, deprecation, display name, relationship descriptors

Write (require `confirmed: true`)

#	Tool	What it does
2	`create_offer_metadata_fieldgroup`	Creates XDM fieldgroup from CSV columns, attaches to decisioning schema. Checks for duplicates first.
3	`bulk_create_offers`	Creates offer items from CSV rows. Supports `dry_run: true` for payload preview
4	`create_collection`	Creates offer collection with filter constraint
5	`create_eligibility_rule`	Creates PQL eligibility rule
6	`create_ranking_formula`	Creates ranking formula (static, custom field, recency-hybrid, custom PQL)
7	`create_selection_strategy`	Wires collection + rule + formula into a selection strategy
8	`create_placement`	Creates channel placement via `/exd-placements` endpoint
11	`update_offer_item`	JSON Patch update on any offer field
12	`add_schema_field`	Adds a single field to an existing tenant fieldgroup
13	`deprecate_schema_field`	Sets `meta:status: deprecated` on a custom tenant field
14	`deprecate_oob_field`	Creates `xdm:descriptorDeprecated` for OOB Adobe-managed fields
15	`detach_fieldgroup`	Removes fieldgroup from schema `allOf` and `meta:extends`

Confirmation pattern

Every write tool shows a preview and blocks with:

⚠️  CONFIRMATION REQUIRED — no changes made yet
[preview of what will happen]
✅ To proceed, call this tool again with confirmed: true

Call the same tool again with confirmed: true to execute.

Recommended workflow from a fresh CSV

1.  parse_csv_and_suggest            → analyse CSV, no writes
2.  list_schema_fieldgroups          → check if fieldgroup already exists
3.  create_offer_metadata_fieldgroup → push schema fields (confirmed: true)
4.  lookup_decisioning_schema        → verify fields attached
5.  bulk_create_offers (dry_run)     → preview offer payloads
6.  bulk_create_offers (confirmed)   → create offers
7.  list_offer_items                 → verify
8.  create_collection                → group offers (confirmed: true)
9.  create_eligibility_rule          → targeting (confirmed: true)
10. create_ranking_formula           → ranking logic (confirmed: true)
11. create_selection_strategy        → wire it all together (confirmed: true)
12. create_placement                 → define channel (confirmed: true)
13. get_setup_summary                → verify full setup

Sample CSV for testing

name,description,category,brand,discount_percent,price,region,priority,start_date,end_date
Summer Glow Kit,Complete summer skincare set,Skincare,GlowCo,20,49.99,US,1,2024-06-01,2024-08-31
SPF Starter Bundle,SPF 30 and 50 combo,Skincare,GlowCo,15,29.99,US,2,2024-06-01,2024-09-30
Loyalty 20% Off,Exclusive 20% for gold members,Discount,GlowCo,20,0,Global,1,2024-01-01,2024-12-31

Column mapping:

name → itemName (OOB), description → itemDescription, priority → itemPriority, start_date/end_date → itemCalendarConstraints
everything else → _<tenant>.<column> (custom fieldgroup)

File layout

exd-mcp-server-without-auth/
├── src/
│   ├── server.js         ← buildMcpServer(config) + 21 tool definitions
│   ├── stdio.js          ← stdio entry (npm start) — for Claude Desktop
│   └── http-local.js     ← local HTTP server for testing the Vercel route
├── api/
│   └── mcp.js            ← Vercel serverless route (Streamable HTTP)
├── scripts/
│   └── smoke.js          ← smoke test for stdio + HTTP transports
├── vercel.json
├── package.json
├── .env.example
└── .gitignore

Smoke testing

npm install
cp .env.example .env       # fill in
npm run smoke              # runs stdio + HTTP transport tests, calls real Adobe APIs

Expected output ends with All smoke checks passed.

Troubleshooting

Symptom	Likely cause	Fix
`Missing credentials`	CLIENT_ID/CLIENT_SECRET not set	Add to `.env` (local) or to your MCP client's header config (deployed)
`IMS token mint failed (401)`	Credentials invalid or revoked	Regenerate the OAuth Server-to-Server credential in Adobe Developer Console
`401 Oauth token is not valid` from Adobe	Credential lacks AEP access	The OAuth credential's product profile needs `Adobe Experience Platform` access for the target sandbox
`403 Forbidden`	Wrong org/sandbox	Check `ORG_ID` and `SANDBOX_NAME`
List offers returns 0	Wrong `ITEM_CATALOG_ID` for the sandbox	Each sandbox has its own catalog ID
Tool call exceeds 10s on Vercel Hobby	Bulk operation too large	Upgrade to Pro (60s) or chunk the CSV
`lookup_decisioning_schema` shows 0 fieldgroups	Resolved by 2.0 — file an issue if you still see this	—

What this MCP does NOT do (future scope)

Decisioning policy / campaign creation — creates components but not the final AJO policy that ties strategy + placement.
Delete operations — AEP recommends archive over delete.
Audience creation — eligibility rules reference profile attributes but don't create AEP segments.
Cross-channel coherence scoring — would require AEP Query Service integration.

Install Server

license - not found

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Tools

View all tools

Latest Blog Posts

Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly
Lightport: Open-Sourcing Glama's AI Gateway
By punkpeye on April 27, 2026.
OpenAI
open source
Tool Definition Quality Score (TDQS)
By punkpeye on April 3, 2026.
mcp

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/khushi-nayal/Decisioning-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server