# MediaWatch — media release monitor (`digigecko/mediawatch`) Actor

Watch any list of TV shows & movies and get alerted the moment a release date or availability status changes. Pay per event — only when something actually happens.

- **URL**: https://apify.com/digigecko/mediawatch.md
- **Developed by:** [Gecko Labs](https://apify.com/digigecko) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.01 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## MediaWatch 🎬 — media release monitor (Apify pay-per-event actor)

Status: **SPIKED + LOCAL PROOF**. Not yet deployed (awaiting Apify token + TMDB key).

A serverless Apify Actor that monitors a list of TV/movie titles and emits a
**pay-per-event `release_change` event** whenever a release date or availability
status changes. Billed per event via Apify Pay-Per-Event (PPE).

### How it works

- Input: list of `{name, type, year?, tmdbId?}`.
- Engine (source-agnostic) fingerprints each title's release state.
- Persists last-known state in the actor's key-value store.
- Any changed field → one `release_change` EVENT row (the PPE billing unit).
- Snapshot rows are always written (free; doubles as demo/health data).

### Files

- `src/engine.js` — core, source-agnostic state engine (unit-testable)
- `src/tmdb.js` — live TMDB provider (needs `TMDB_API_KEY`)
- `test/mock-provider.js` + `test/spike.mjs` — offline proof (no keys)
- `.actor/` — Apify actor + input schema; `Dockerfile` — deploy image
- `main.js` — Apify SDK entry

### Run locally (spike, no keys)

```bash
node test/spike.mjs
```

### Deploy checklist (blocked)

1. \[ ] Apify account + API token (from Juan)
2. \[ ] TMDB API read token, set as `TMDB_API_KEY` (from Juan)
3. \[ ] `apify push` to deploy, set `provider=tmdb`
4. \[ ] In Apify console: define the PPE event type (`release_change`) + pricing
5. \[ ] Publish to Store with free-tier funnel + PPE pricing

### PP-Economics (2026)

- 80% dev split; $20/$100 payout minimums. Store avg ~$470/mo/dev.
- PPE-only (Apify kills flat rentals 2026-10-01). Free tier engages users first.

# Actor input Schema

## `titles` (type: `array`):

List of TV shows / movies to watch. Each item: { name, type: 'tv'|'movie', year?, tmdbId? }

## `provider` (type: `string`):

tmdb = live release data (requires TMDB\_API\_KEY below). mock = deterministic test data (no external calls).

## `tmdbApiKey` (type: `string`):

TMDB v3 read token. Required only when provider=tmdb. Stored as a secret; never shown back or logged.

## Actor input object example

```json
{
  "titles": [
    {
      "name": "The Last Days of Ptolemy Grey",
      "type": "tv"
    },
    {
      "name": "Severance",
      "type": "tv"
    }
  ],
  "provider": "mock"
}
```

# Actor output Schema

## `releases` (type: `string`):

Default dataset: one row per detected release\_change event (fields: eventType, title, type, previous, current, delta, at), plus one {kind:'snapshot'} row per run summarizing every monitored title's current release fingerprint (status, releaseDate, available, nextEpisode, nextEpisodeAirDate, lastEpisodeAirDate, totalEpisodes).

## `lastRun` (type: `string`):

Key-value store record `lastRun` — ISO timestamp of the most recent completed run. Useful as a health/heartbeat check for scheduled monitoring.

# 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 = {
    "titles": [
        {
            "name": "The Last Days of Ptolemy Grey",
            "type": "tv"
        },
        {
            "name": "Severance",
            "type": "tv"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("digigecko/mediawatch").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 = { "titles": [
        {
            "name": "The Last Days of Ptolemy Grey",
            "type": "tv",
        },
        {
            "name": "Severance",
            "type": "tv",
        },
    ] }

# Run the Actor and wait for it to finish
run = client.actor("digigecko/mediawatch").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 '{
  "titles": [
    {
      "name": "The Last Days of Ptolemy Grey",
      "type": "tv"
    },
    {
      "name": "Severance",
      "type": "tv"
    }
  ]
}' |
apify call digigecko/mediawatch --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,digigecko/mediawatch"
        }
    }
}
```

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/t56jgFxYzbyvJu9G4/builds/fWarop4Z0gf8siq0A/openapi.json