Amazon Seller MCP Server - DataDoe
by Deltologic
README.md
# Amazon Seller MCP Server - DataDoe
> **Hosted Amazon Seller Central & Vendor Central MCP server with read and write access.** Connect Claude, ChatGPT, Cursor, Codex, Gemini, and GitHub Copilot to live Amazon SP-API and Amazon Ads API data, then let your AI agent act on it: update listings, manage orders, and optimize Amazon Ads campaigns. DataDoe handles the SP-API developer approval, OAuth, and rate limits so your AI agent starts working in under a minute.
[](https://www.datadoe.com/)
[](https://developer-docs.amazon.com/sp-api/)
[](https://advertising.amazon.com/API/docs)
[](https://modelcontextprotocol.io/)
[](#actions-write-to-amazon)
[](#quick-setup-per-ai-client)
[](https://smithery.ai/servers/jakopv007/datadoe-mcp)
[](LICENSE)
🔗 [Start a free trial](https://www.datadoe.com/connect/amazon/mcp) · 📘 [Documentation](https://www.datadoe.com/hub/docs) · ⚡ [Actions](https://www.datadoe.com/hub/docs/datadoe-features/actions) · 📊 [Amazon data schema](https://www.datadoe.com/hub/data-scheme) · 🎥 [Video demo](https://www.youtube.com/watch?v=9YQd7M2dMyY)
---
## 🚀 Quick start
1. Sign up at [DataDoe](https://www.datadoe.com/connect/amazon/mcp) and connect your Amazon Seller Central or Vendor Central account.
2. Create a DataDoe MCP API key in [DataDoe MCP Integrations](https://app.datadoe.com/integrations/mcp).
3. Paste the config below into your AI client (Claude, Cursor, Codex, Gemini, GitHub Copilot, ChatGPT, or any MCP-capable tool):
```json
{
"mcpServers": {
"datadoe": {
"url": "https://mcp.datadoe.com/mcp/v1",
"headers": {
"datadoe-mcp-key": "<YOUR_DATADOE_MCP_KEY>"
}
}
}
}
```
4. Ask your AI agent: *"Show my top 10 ASINs by revenue last month across all Amazon marketplaces."*
That's it. DataDoe runs the MCP server on hosted infrastructure, so your team doesn't need to deploy anything locally or wait for Amazon SP-API developer approval.
---
## What is DataDoe MCP?
**DataDoe MCP** is a hosted [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) server for **Amazon sellers, vendors, and agencies**. It exposes your live Amazon Selling Partner API (SP-API) and Amazon Ads API data through MCP tools that work with Claude, ChatGPT, Cursor, Codex CLI, Gemini CLI, GitHub Copilot, Claude Desktop, n8n, NanoClaw, and any other MCP-capable client.
DataDoe MCP gives your AI agent two layers over your Amazon account:
- **A read layer** - SKU-level orders, sales, ads spend, traffic, inventory, listings, returns, settlements, brand analytics, and catalog, returned as structured tool responses or downloadable CSV and JSON exports.
- **A write layer (Actions)** - your agent can change your Amazon account through the SP-API and Amazon Ads API: update listings, cancel orders, confirm shipments, and manage Amazon Ads campaigns, ad groups, targets, and ads.
Building your own Amazon SP-API integration typically requires SP-API developer registration, OAuth refresh-token flow, marketplace-specific endpoints, throttling logic, and 2-4 weeks of Amazon approval. DataDoe takes care of all of that. You get a single authenticated MCP URL for both reading and acting on your Amazon data.
## Who is DataDoe MCP for?
- **Amazon sellers (FBA, FBM, multi-marketplace)** - get instant answers from your own seller data without context-switching to Seller Central.
- **Amazon agencies** managing multiple client accounts - query across every connected Seller Central / Vendor Central account from one MCP server.
- **Vendors with Vendor Central** - read 1P data alongside 3P data with the same tools.
- **AI builders and developers** - ship Amazon-aware AI agents, dashboards, and internal tools without writing your own SP-API integration.
- **Operations teams** - automate recurring reports via Claude Code, Cursor, n8n, or any MCP-capable workflow tool.
## Why use DataDoe MCP?
- ✅ **No SP-API approval needed** - DataDoe handles SP-API developer registration, OAuth, refresh tokens, and rate limits on your behalf.
- ✅ **30-second setup** - paste the MCP URL and your API key into your AI client config. DataDoe runs the server on hosted infrastructure.
- ✅ **20+ documented integrations** out of the box: Claude, ChatGPT, Cursor, Codex, Gemini CLI, GitHub Copilot, n8n, CrewAI, the Claude & OpenAI Agent SDKs, Excel / Word / PowerPoint via Claude, and any other MCP-capable client.
- ✅ **SKU-level resolution** - drill into individual ASINs, parent listings, marketplaces, time periods, ad campaigns, keyword reports, settlements, returns.
- ✅ **Multi-marketplace, multi-account** - one MCP server covers every Amazon marketplace (US, UK, DE, FR, IT, ES, CA, AU, JP, MX, and more) across Seller Central and Vendor Central.
- ✅ **AI-native by design** - `exports_create` accepts SQL-like filter groups, GROUP BY, aggregations, and date intervals, so your AI agent can build complex reports from one tool call.
- ✅ **Read and write** - with [Actions](#actions-write-to-amazon), your agent doesn't just report, it updates listings, manages orders, and optimizes Amazon Ads, with a `dryRun` validation step and per-type controls.
- ✅ **Always-on hosted infrastructure** - DataDoe manages SP-API rate limits, token rotation, and ongoing maintenance.
## What can you ask DataDoe MCP?
Example questions your AI agent can answer with DataDoe MCP connected:
- *"What were my top 20 ASINs by revenue last month across all Amazon marketplaces?"*
- *"Reconcile my Amazon settlements against orders for Q1, and show any discrepancies."*
- *"Which Amazon Ads campaigns had ACoS over 40% last week, and what was their total spend?"*
- *"Show inventory units at risk of stocking out in the next 14 days."*
- *"Build me a daily KPI dashboard with sales, traffic, and ad spend for the last 90 days."*
- *"Which search terms in my PPC reports drove the most clicks but zero conversions?"*
- *"Pull every Amazon return for SKU ABC-123 in the last 60 days and summarize the return reasons."*
- *"Compare my brand analytics search term share-of-voice month over month."*
And with **Actions** enabled, your agent can act on what it finds:
- *"Raise the daily budget on my top-ACoS Sponsored Products campaign by 20%."*
- *"Pause every campaign with ACoS over 50% last week."*
- *"Update the price of SKU ABC-123 to 19.99 and refresh its bullet points."*
- *"Confirm shipment for order 123-4567890-1234567 with UPS tracking 1Z999..."*
## Actions: write to Amazon
Actions let your AI agent make changes on your connected Amazon Seller Central, Vendor Central, and Amazon Ads accounts through the SP-API and Amazon Ads API. Every Action is recorded and auditable.
What your agent can do:
- **Listings** - update the title, bullet points, description, price, generic keyword, and item-type keyword (`AMAZON_LISTINGS_UPDATE`).
- **Orders** - cancel an order item with a reason (`AMAZON_ORDERS_CANCEL`) or confirm shipment and upload tracking (`AMAZON_ORDERS_CONFIRM_SHIPMENT`).
- **Amazon Ads** - add, update, remove, and find campaigns, ad groups, targets, ads, and ad associations across Sponsored Products, Brands, Display, TV, and Amazon DSP.
How it works - the agent runs each Action through these MCP tools:
1. `actions_details_schema_get` - get the payload schema for the Action type.
2. `actions_start` with `dryRun=true` - validate the payload without executing.
3. `actions_start` - run the Action and get an action id.
4. `actions_get` - poll until it completes, then read the `result`.
Use `actions_list` to review past Actions (filter by status, type, creator, and date).
Action types are disabled by default and enabled per type in [Settings > Actions](https://app.datadoe.com/settings?tab=actions). When a type is disabled, `actions_start` rejects live runs but still allows `dryRun` validation.
Example `details` payload for `AMAZON_LISTINGS_UPDATE`:
```json
{
"type": "AMAZON_LISTINGS_UPDATE",
"sellerOrVendorId": "<SELLER_OR_VENDOR_UUID>",
"dryRun": true,
"details": {
"changes": [
{
"sku": "ABC-123",
"language_tag": "en_US",
"name": "Stainless Steel Water Bottle 750ml",
"price": 19.99
}
]
}
}
```
Running an Action costs 2 AI tokens for up to 100 entities, plus 1 token per additional 100. See the [Actions docs](https://www.datadoe.com/hub/docs/datadoe-features/actions) for the full catalog and payload schemas.
## Available MCP tools
DataDoe MCP exposes the following tools to your AI client:
| Tool | Category | What it does |
|---|---|---|
| `sellers_and_vendors_list` | Account | Lists Amazon sellers and vendors connected to your DataDoe organization, with pagination and optional filters by name and marketplace country code. |
| `organization_and_subscription_details_get` | Account | Returns your DataDoe organization profile and active subscription plan. |
| `exports_sources_get` | Data | Searches DataDoe's catalog of pre-built Amazon data export templates (orders, sales and traffic, ads performance, inventory, listings, settlements, returns, brand analytics, and more). |
| `exports_create` | Data | Creates an Amazon data export from any source. Supports SQL-like filters, GROUP BY, aggregations (sum / avg / count / countDistinct / min / max), date intervals (DAY / WEEK / MONTH), pagination, and CSV or JSON output. |
| `exports_get` | Data | Returns status and metadata for an in-flight or completed export job. |
| `exports_list` | Data | Lists export jobs for the organization with pagination and optional export ID filters. |
| `exports_raw_url_get` | Data | Returns a one-time download URL served by the DataDoe MCP server for a completed export. |
| `exports_raw_download` | Data | Returns the raw export content (CSV or JSON) inline in the tool response. |
| `exports_delete` | Data | Deletes an export by its ID. |
| `files_create` | Files | Creates and uploads a utility file (listing or A+ images) as base64-encoded content. |
| `files_list` | Files | Lists utility files for the organization with pagination and filters. |
| `files_get` | Files | Returns metadata for a utility file by id. |
| `files_download_url_get` | Files | Returns a one-time download URL for an uploaded file. |
| `files_delete` | Files | Deletes a utility file and its stored object when present. |
| `datadoe_user_docs_table_of_contents_get` | Docs | Returns the table of contents of the DataDoe user documentation, useful when an agent needs to look up features or capabilities on demand. |
| `datadoe_user_docs_page_get` | Docs | Returns the full content of a named DataDoe documentation page. |
| `actions_details_schema_get` | Actions | Returns the JSON Schema of the `details` payload required to start a given Action type. |
| `actions_start` | Actions | Starts an Action that changes your Amazon account (listings, orders, Amazon Ads). Set `dryRun=true` to validate without executing. Returns an action id. |
| `actions_get` | Actions | Returns the status and `result` of an Action by id; poll after `actions_start`. |
| `actions_list` | Actions | Returns paginated Action history, filterable by status, type, creator, and date. |
| `cogs_upsert` | COGS | Creates or updates cost-of-goods-sold rows for a seller or vendor. |
| `cogs_delete` | COGS | Deletes COGS rows for a seller or vendor with optional filters. |
| `vendor_code_upsert` | Vendor codes | Creates or updates vendor code rows for a seller or vendor. |
| `vendor_code_delete` | Vendor codes | Deletes vendor code rows for a seller or vendor with optional filters. |
| `plugins_get` | Plugins | Returns enabled DataDoe Plugins (Memories, Skills, and Files) for the user. |
| `plugins_memories_create` | Plugins | Creates a memory Plugin for the user or organization. |
| `plugins_memories_edit` | Plugins | Updates a memory Plugin for the user or organization. |
| `plugins_skills_get` | Plugins | Returns a Skill element (SKILL.md or supporting file) listed by plugins_get. |
| `plugins_files_get` | Plugins | Returns converted markdown content for a File plugin listed by plugins_get. |
---
## Quick setup per AI client
The snippets below are the minimum config you need. For step-by-step guides per AI client, see the [Per-client setup guides](#per-client-setup-guides) list at the end of this section.
### Claude Desktop · Claude.ai
```json
{
"mcpServers": {
"datadoe": {
"url": "https://mcp.datadoe.com/mcp/v1",
"headers": {
"datadoe-mcp-key": "<YOUR_DATADOE_MCP_KEY>"
}
}
}
}
```
### Claude Code
```bash
claude mcp add datadoe \
--transport http \
--url https://mcp.datadoe.com/mcp/v1 \
--header "datadoe-mcp-key: <YOUR_DATADOE_MCP_KEY>"
```
### Cursor
Add to `~/.cursor/mcp.json` (global) or `.cursor/mcp.json` (per project):
```json
{
"mcpServers": {
"datadoe": {
"url": "https://mcp.datadoe.com/mcp/v1",
"headers": {
"datadoe-mcp-key": "<YOUR_DATADOE_MCP_KEY>"
}
}
}
}
```
### GitHub Copilot (VS Code)
Add to your VS Code `mcp.json`:
```json
{
"mcpServers": {
"datadoe": {
"url": "https://mcp.datadoe.com/mcp/v1",
"headers": {
"datadoe-mcp-key": "<YOUR_DATADOE_MCP_KEY>"
}
}
}
}
```
### Codex CLI · Gemini CLI · ChatGPT · n8n · NanoClaw · any other MCP client
DataDoe MCP works as a **generic remote MCP server**. Configure your client with:
- **URL**: `https://mcp.datadoe.com/mcp/v1`
- **Transport**: HTTP Streamable
- **Auth header**: `datadoe-mcp-key: <YOUR_DATADOE_MCP_KEY>` (create a key in [DataDoe MCP Integrations](https://app.datadoe.com/integrations/mcp))
### Per-client setup guides
For step-by-step setup guides per AI client, see the dedicated DataDoe documentation pages:
- [Using Claude](https://www.datadoe.com/hub/docs/datadoe-mcp/claude)
- [Using ChatGPT](https://www.datadoe.com/hub/docs/datadoe-mcp/chatgpt)
- [Using Claude Code](https://www.datadoe.com/hub/docs/datadoe-mcp/claude-code)
- [Using Claude Agent SDK](https://www.datadoe.com/hub/docs/datadoe-mcp/claude-agents-sdk)
- [Using Codex](https://www.datadoe.com/hub/docs/datadoe-mcp/codex)
- [Using Codex Sites](https://www.datadoe.com/hub/docs/datadoe-mcp/codex-sites)
- [Using CrewAI](https://www.datadoe.com/hub/docs/datadoe-mcp/crewai)
- [Using Cursor](https://www.datadoe.com/hub/docs/datadoe-mcp/cursor)
- [Using Excel + Claude](https://www.datadoe.com/hub/docs/datadoe-mcp/excel)
- [Using Gemini CLI](https://www.datadoe.com/hub/docs/datadoe-mcp/gemini-cli)
- [Using Gumloop](https://www.datadoe.com/hub/docs/datadoe-mcp/gumloop)
- [Using Hermes Agent](https://www.datadoe.com/hub/docs/datadoe-mcp/hermes)
- [Using n8n](https://www.datadoe.com/hub/docs/datadoe-mcp/n8n)
- [Using NanoClaw](https://www.datadoe.com/hub/docs/datadoe-mcp/nanoclaw)
- [Using OpenAI Agents SDK](https://www.datadoe.com/hub/docs/datadoe-mcp/openai-agents-sdk)
- [Using OpenClaw](https://www.datadoe.com/hub/docs/datadoe-mcp/openclaw)
- [Using OpenCode](https://www.datadoe.com/hub/docs/datadoe-mcp/opencode)
- [Using PowerPoint + Claude](https://www.datadoe.com/hub/docs/datadoe-mcp/powerpoint)
- [Using VS Code](https://www.datadoe.com/hub/docs/datadoe-mcp/vs-code)
- [Using Word + Claude](https://www.datadoe.com/hub/docs/datadoe-mcp/word)
Full documentation root: [www.datadoe.com/hub/docs](https://www.datadoe.com/hub/docs)
---
## What Amazon data is available?
DataDoe MCP exposes every Amazon data table available in DataDoe, including:
- **Orders & sales**: order line items, order performance, sales and traffic, refunds
- **Amazon Ads**: campaigns, ad groups, keywords, search terms, sponsored products / brands / display, ACoS / ROAS / impression-share metrics
- **Inventory**: FBA inventory, restock recommendations, stranded inventory, age, units at risk
- **Listings & catalog**: ASIN catalog, product attributes, buy box ownership, variations
- **Finance**: settlements, fees, reserves, reimbursements, deposits
- **Returns**: customer returns, return reasons, FBA returns
- **Brand analytics**: search query performance, market basket, repeat purchase, demographics
- **Traffic**: sessions, page views, conversion rate, by ASIN, by marketplace
Full schema: [www.datadoe.com/hub/data-scheme](https://www.datadoe.com/hub/data-scheme)
---
## What DataDoe MCP is NOT
To avoid confusion when evaluating Amazon MCP servers:
- ❌ **Not a self-hosted MCP server.** DataDoe MCP is hosted at `mcp.datadoe.com`. This GitHub repository is the schema facade used to publish DataDoe MCP to public MCP registries (Official MCP Registry, mcpservers.org, glama.ai, mcpmarket.com, Cline, smithery.ai, and others).
- ❌ **Not an SP-API wrapper you operate yourself.** DataDoe owns the SP-API developer registration, OAuth flow, refresh-token rotation, marketplace endpoints, and rate-limit handling. You bring your Amazon account; we handle the rest.
- ❌ **Not a free open-source server.** DataDoe MCP requires a DataDoe subscription. For a free self-hosted alternative, see the community Amazon MCP servers indexed at [mcpservers.org](https://mcpservers.org/).
---
## Documentation & resources
- [DataDoe homepage](https://www.datadoe.com/)
- [DataDoe documentation](https://www.datadoe.com/hub/docs)
- [DataDoe Actions (write to Amazon)](https://www.datadoe.com/hub/docs/datadoe-features/actions)
- [Amazon data schema reference](https://www.datadoe.com/hub/data-scheme)
- [DataDoe MCP product page](https://www.datadoe.com/connect/amazon/mcp)
- [DataDoe vs Amazon MCP comparison](https://www.datadoe.com/compare/datadoe-vs-amazon-mcp)
- [DataDoe MCP video demo](https://www.youtube.com/watch?v=9YQd7M2dMyY)
- [DataDoe blog: Amazon SP-API, Ads API, and MCP explainers](https://www.datadoe.com/blog)
- [Create a DataDoe MCP API key](https://app.datadoe.com/integrations/mcp)
---
## License
[MIT](LICENSE). The DataDoe MCP schema facade in this repository is open-source. The hosted MCP server, DataDoe application, and DataDoe infrastructure are proprietary and operated by Deltologic.
---
### How to start and connect locally (for indexing and listing)
> This server is just a schema facade of the actual DataDoe MCP server, made for exposing DataDoe MCP to various MCP registries.
> It is a no-op server: it does not do anything beyond exposing the schema of DataDoe MCP.
> If you want to actually use DataDoe MCP, see the [DataDoe MCP documentation](https://www.datadoe.com/hub/docs).
#### 1. Install dependencies
```bash
yarn install
```
#### 2. Build the local server
```bash
yarn build
```
This compiles the TypeScript source into `dist/index.js`.
#### 3. Start the server
```bash
yarn start
```
The server communicates over stdio, so the process is intended to be launched by your MCP client rather than opened in a browser.
#### 4. Connect from an MCP client
For a local setup, point your client at the built entrypoint. A typical `mcp.json` looks like this:
```json
{
"mcpServers": {
"datadoe": {
"command": "node",
"args": ["/absolute/path/to/datadoe-mcp/dist/index.js"]
}
}
}
```
If your client supports environment-based workspace paths, you can usually swap the absolute path for the actual clone location on your machine.
---
DataDoe is operated by [Deltologic](https://github.com/Deltologic) · [datadoe.com](https://www.datadoe.com/)
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