# US MAP Retail Evidence (`zinin/us-map-retail-evidence`) Actor

Process supplied US retail catalog evidence into deterministic MAP, availability, baseline, and review findings.

- **URL**: https://apify.com/zinin/us-map-retail-evidence.md
- **Developed by:** [Tim Zinin](https://apify.com/zinin) (community)
- **Categories:** MCP servers, Automation, E-commerce
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $42.50 / 1,000 evidence reports

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## US MAP Retail Evidence

US retail catalog and minimum-advertised-price evidence processor for supplied
Apify Dataset rows. It produces one deterministic report containing exact-ID
evidence, MAP status, seller authorization, availability changes, absence
assessments, and review actions that are explicitly not sent.

### Quick start

Use exactly one source: inline `rows` or a buyer Dataset ID.

```json
{
  "schemaVersion": "1.0",
  "requestId": "auto",
  "watchId": "lego-watch",
  "watchUniverse": ["tcin:16747749"],
  "rows": [
    {
      "adapter": "target",
      "sourceRecordId": "target-16747749",
      "sourceName": "target_probe",
      "observedAt": "2026-08-06T11:03:21.195Z",
      "data": {
        "tcin": "16747749",
        "upc": "673419233606",
        "productTitle": "LEGO Classic Large Creative Brick Box",
        "brand": "LEGO",
        "price": null,
        "availability": null,
        "url": "https://www.target.com/p/example/-/A-16747749"
      },
      "mapCents": 2999
    }
  ],
  "options": {"maxItems": 100, "explanation": "off", "analysisModel": null}
}
```

This example keeps Target's null price and availability as null. For a
canonical row use `adapter: "canonical_rows"`, one exact identity field, and
the same source metadata. The native mappings are grounded in the committed
probe fixtures for Google Shopping, Amazon, Walmart product detail, and Target.
Only a public USD integer-cent base price can produce `below_map`; restricted,
member, coupon, non-USD, fractional-cent, and conflicting-variant prices stay
indeterminate.

`requestId: "auto"` is a task-safe sentinel. On Apify it resolves before input
validation to the trusted `actorRunId`, so each Actor run has its own delivery
idempotency scope while a replay of that same run never charges twice. Explicit
non-`auto` IDs keep their existing idempotency behavior. A remote run fails
closed if Apify cannot provide `actorRunId`; the deterministic fallback exists
only in test mode.

### Output and semantics

One useful run writes exactly one report Dataset item with
`pushData(report, "result-found")`. Empty, invalid, all-unavailable, or
insufficient input writes only `OUTPUT`. The terminal statuses are
`complete`, `complete_empty`, `partial_pagination`, `partial_budget`,
`source_unavailable`, `delivery_unknown`, `bootstrap_required`, and `bootstrap_retry`. The two
bootstrap statuses and `complete_empty` are free:
a new watch creates and verifies one persistent RequestQueue seed, then asks the
caller to retry the same input after `bootstrap.retryAfterMs`. Bootstrap never
reads evidence, writes a report or baseline, or emits `result-found`.
For a closed Dataset with `expectedItems: 0`, `complete_empty` truthfully marks
every configured identity absent, writes only `OUTPUT` plus a free baseline
snapshot, and emits no `result-found` event.
Partial or unavailable runs do not advance the watch baseline. A complete run
advances it only after the result counter confirms exactly one charge and the
delivery receipt is persisted. `watchUniverse` is the configured exact-ID
universe, not the current observed set, so deletions compare correctly. Dataset
input additionally requires `datasetCoverage: {"closed": true,
"expectedItems": N}` matching the bounded read; unproven coverage is never
complete. Baselines are compared only when Actor identity, version, adapter
version, query scope, and entity identity all match. Query scope is derived
from the configured source, limits, adapters, and `watchUniverse`; observed
seller/restriction policy is retained as a per-identity baseline binding.
Therefore a complete disappearance can be assessed as `absent`, while a policy
reassignment or change for a still-present identity is noncomparable, emits an
unsent review action, and suppresses MAP evidence for that identity. The lock uses a separate
named RequestQueue per watch and preserves the verified seed between runs. Once
the seed is stable, the RequestQueue client atomically acquires its lease with
`listAndLockHead()` and releases it with `deleteRequestLock(requestId)`.
The client heartbeat renews the real request lock when the SDK exposes
`prolongRequestLock()`. A replay of a confirmed or
reconciliation-required request does not push or charge again.

`delivery_unknown` means the delivery side effect may have happened but cannot
be proven from the durable receipt and exact counter delta. It is distinct from
`source_unavailable`, never confirms payment, and is never retried automatically.
If the persistent RequestQueue lock cannot be released after delivery, the
confirmed receipt and counters remain truthful but `OUTPUT` is downgraded to
`delivery_unknown` and the Actor fails with a nonzero process state instead of
exiting as a clean success, even if the failure hook is unavailable.
There is no live source discovery: supplied URLs are never requested. There are
no nested Actor calls, browser sessions, proxies, or fuzzy product joins.
Review actions carry `review_required: true` and `not_sent: true`; this Actor
never sends them.

### Pricing

| Tier | Discount | Start | `result-found` |
|---|---:|---:|---:|
| FREE | 0% | $0.00500 | $0.05000 |
| BRONZE | 5% | $0.00475 | $0.04750 |
| SILVER | 10% | $0.00450 | $0.04500 |
| GOLD | 15% | $0.00425 | $0.04250 |
| PLATINUM | 18% | $0.00410 | $0.04100 |
| DIAMOND | 20% | $0.00400 | $0.04000 |

The default Dataset item event is absent. `maxTotalChargeUsd` is checked before
the paid write. Optional explanation mode is caller-funded BYOK and does not
change deterministic facts or billing.

### API and MCP

The standard Apify Actor API accepts the JSON input above. The package metadata
is MCP-compatible, but this package does not claim a separate MCP server,
remote tool registration, or payment endpoint.

```bash
curl -sS -X POST \
  'https://api.apify.com/v2/acts/zinin~us-map-retail-evidence/runs?waitForFinish=60' \
  -H 'Authorization: Bearer YOUR_APIFY_TOKEN' \
  -H 'Content-Type: application/json' \
  --data @input.json
```

### Related Actors

| Actor | Role in a truthful workflow |
|---|---|
| [Structured Extract](https://apify.com/zinin/structured-extract) | Prepare typed evidence rows before this Actor evaluates a supplied catalog. |
| [Shopify Price Change Monitor](https://apify.com/zinin/shopify-price-change-monitor) | Supply complementary Shopify price observations for a separate watch. |
| [US Brand Offer Evidence Normalizer](https://apify.com/zinin/us-brand-offer-evidence-normalizer) | Normalize supplied promotion evidence before comparison. |
| [Lead List Cleaner](https://apify.com/zinin/lead-list-cleaner) | Clean downstream retailer or seller lead records after review. |

These are links for workflow composition only. This Actor never calls them.

### SEO

Canonical title: `US MAP Retail Evidence | Deterministic Retail MAP Monitoring`.

Meta description: `Process supplied US retail catalog evidence into exact-ID MAP,
availability, baseline, and review findings without browsing or nested Actor
calls.`

Keywords: `US MAP monitoring`, `minimum advertised price evidence`, `retail
catalog evidence`, `Apify Dataset processor`, `seller authorization`.

# Actor input Schema

## `schemaVersion` (type: `string`):

The supported input contract version.

## `requestId` (type: `string`):

Use auto for one idempotency scope per trusted Apify Actor run; explicit IDs preserve replay idempotency.

## `watchId` (type: `string`):

Stable watch identity used to scope the deletion-safe baseline.

## `watchUniverse` (type: `array`):

Exact identity keys expected in the closed snapshot; this is used for deletion-safe baseline comparison.

## `rows` (type: `array`):

One to 100 supplied rows; use this or datasetId.

## `datasetId` (type: `string`):

Read-only Dataset selected by the buyer; one bounded first page is read.

## `datasetCoverage` (type: `object`):

Required for Dataset input. The declared count must equal the bounded read and closed must be true.

## `openrouterApiKey` (type: `string`):

Secret caller-owned key used only for one explanation request.

## `options` (type: `object`):

Optional bounded processing and explanation settings.

## Actor input object example

```json
{
  "schemaVersion": "1.0",
  "requestId": "auto",
  "watchId": "lego-watch",
  "watchUniverse": [
    "tcin:16747749"
  ],
  "rows": [
    {
      "adapter": "target",
      "sourceRecordId": "target-16747749",
      "sourceName": "target_probe",
      "observedAt": "2026-08-06T11:03:21.195Z",
      "data": {
        "tcin": "16747749",
        "upc": "673419233606",
        "productTitle": "LEGO Classic Large Creative Brick Box",
        "brand": "LEGO",
        "price": null,
        "availability": null,
        "url": "https://www.target.com/p/example/-/A-16747749"
      },
      "mapCents": 2999
    }
  ],
  "options": {
    "maxItems": 100,
    "explanation": "off",
    "analysisModel": null
  }
}
```

# Actor output Schema

## `OUTPUT` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "schemaVersion": "1.0",
    "requestId": "auto",
    "watchId": "lego-watch",
    "watchUniverse": [
        "tcin:16747749"
    ],
    "rows": [
        {
            "adapter": "target",
            "sourceRecordId": "target-16747749",
            "sourceName": "target_probe",
            "observedAt": "2026-08-06T11:03:21.195Z",
            "data": {
                "tcin": "16747749",
                "upc": "673419233606",
                "productTitle": "LEGO Classic Large Creative Brick Box",
                "brand": "LEGO",
                "price": null,
                "availability": null,
                "url": "https://www.target.com/p/example/-/A-16747749"
            },
            "mapCents": 2999
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("zinin/us-map-retail-evidence").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "schemaVersion": "1.0",
    "requestId": "auto",
    "watchId": "lego-watch",
    "watchUniverse": ["tcin:16747749"],
    "rows": [{
            "adapter": "target",
            "sourceRecordId": "target-16747749",
            "sourceName": "target_probe",
            "observedAt": "2026-08-06T11:03:21.195Z",
            "data": {
                "tcin": "16747749",
                "upc": "673419233606",
                "productTitle": "LEGO Classic Large Creative Brick Box",
                "brand": "LEGO",
                "price": None,
                "availability": None,
                "url": "https://www.target.com/p/example/-/A-16747749",
            },
            "mapCents": 2999,
        }],
}

# Run the Actor and wait for it to finish
run = client.actor("zinin/us-map-retail-evidence").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "schemaVersion": "1.0",
  "requestId": "auto",
  "watchId": "lego-watch",
  "watchUniverse": [
    "tcin:16747749"
  ],
  "rows": [
    {
      "adapter": "target",
      "sourceRecordId": "target-16747749",
      "sourceName": "target_probe",
      "observedAt": "2026-08-06T11:03:21.195Z",
      "data": {
        "tcin": "16747749",
        "upc": "673419233606",
        "productTitle": "LEGO Classic Large Creative Brick Box",
        "brand": "LEGO",
        "price": null,
        "availability": null,
        "url": "https://www.target.com/p/example/-/A-16747749"
      },
      "mapCents": 2999
    }
  ]
}' |
apify call zinin/us-map-retail-evidence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,zinin/us-map-retail-evidence"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/qkJCYH8A29bllWgMY/builds/FY6LuB63eCdc3M9hV/openapi.json