# Us Dividend Safety Growth Mcp (`mcp_collector/us-dividend-safety-growth-mcp`) Actor

- **URL**: https://apify.com/mcp_collector/us-dividend-safety-growth-mcp.md
- **Developed by:** [koon](https://apify.com/mcp_collector) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$8.00 / 1,000 dividend safety analysis runs

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

## 💰 US Dividend Safety & Growth Quant Scanner MCP

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**Ultra-fast (<3s) Zero-Proxy MCP Tool for AI Agents (Claude, Cursor, LangChain).**\
Analyzes US dividend yield, net income payout ratio safety, operating cash flow (OCF) dividend coverage, 5-year average yield historical valuation, and computes a quantitative **Dividend Safety Score (0-100)** with Dividend Cut Risk classification in compact JSON and a 1-line Markdown summary. Supports both individual dividend stocks (KO, JNJ, O, MO, PG, AAPL, MSFT) and dividend ETFs (SCHD, VYM, JEPI, DGRO).

***

### ⚡ Why AI Agents Need This MCP

1. **Massive Context Token Savings (<250 tokens vs 20,000+ tokens)**: Raw cashflow statements and annual reports overwhelm LLM context windows. This tool extracts high-signal dividend safety metrics and cash flow coverage in a single serverless call.
2. **Avoids High-Yield "Dividend Traps"**: High dividend yields often signal severe business distress. This scanner checks payout ratios and cash flow coverage to flag cut risks before dividend suspensions happen.
3. **Historical Valuation Anchor**: Compares current yield to the 5-year average yield (`five_year_avg_dividend_yield_percent`) to detect whether a quality stock is historically undervalued.
4. **Pay-Per-Event Micro-Pricing**: Only $0.008 per stock/ETF analyzed ($8.00 / 1,000 queries).

***

### 🎯 The Complete 5-Star Hedge Fund Agent Pipeline

Run an end-to-end institutional grade investment check across our MCP suite:

1. **Macro Check**: Call `macro-regime-liquidity-mcp` ➔ Determine global risk regime (Goldilocks vs Stagflation).
2. **Smart Money Check**: Call `smart-money-insider-flow-mcp` ➔ Confirm institutional backing & CEO insider buying.
3. **Wall Street Consensus**: Call `wallstreet-consensus-target-mcp` ➔ Check IB price targets & 4Q earnings beat streak.
4. **Dividend Safety Check**: Call `us-dividend-safety-growth-mcp` (this tool) ➔ Verify payout safety, cash flow coverage, and dividend cut risk.
5. **Timing & Execution**: Call `us-stock-etf-quant-signal-mcp` ➔ Identify Wilder RSI turnaround, 20MA reclaim, and 2ATR stop-loss levels.

***

### 🔌 Claude Desktop & Cursor MCP Configuration

```json
{
  "mcpServers": {
    "dividend-safety": {
      "command": "npx",
      "args": [
        "-y",
        "@apify/mcp-server",
        "--actor",
        "YOUR_APIFY_USERNAME/us-dividend-safety-growth-mcp"
      ],
      "env": {
        "APIFY_TOKEN": "YOUR_APIFY_API_TOKEN"
      }
    }
  }
}
```

***

### 📦 Output Sample

```json
{
  "ticker": "KO",
  "company_name": "Coca-Cola Company (The)",
  "quote_type": "EQUITY",
  "analyzed_at_kst": "2026-10-31 19:55:00 KST",
  "timestamp_utc": "2026-10-31 10:55:00 UTC",
  "current_price": 94.08,
  "is_dividend_payer": true,
  "dividend_cut_risk": "VERY_LOW_RISK (매우 안전 - 배당 귀족/안정 성장형 우량주)",
  "dividend_safety_score": 88.5,
  "dividend_metrics": {
    "dividend_rate_usd": 2.12,
    "dividend_yield_percent": 2.25,
    "payout_ratio_percent": 61.95,
    "five_year_avg_dividend_yield_percent": 2.83,
    "valuation_status": "FAIRLY_VALUED (5년 역사적 평균 배당수익률 수렴)",
    "ex_dividend_date": "2026-12-01"
  },
  "coverage_and_cashflow": {
    "annual_total_dividends_paid_usd": 9120032950.2,
    "operating_cash_flow_usd": 16227000320.0,
    "free_cash_flow_usd": 4349000192.0,
    "operating_cash_flow_coverage_ratio": 1.78,
    "free_cash_flow_coverage_ratio": 0.48
  },
  "fundamentals": {
    "trailing_eps": 3.39,
    "forward_eps": 3.53,
    "debt_to_equity_percent": 107.45
  },
  "score_breakdown": {
    "payout_ratio_safety_score_weight_40": 34.6,
    "cashflow_and_stability_score_weight_35": 28.9,
    "yield_valuation_score_weight_25": 25.0,
    "total_safety_score": 88.5
  },
  "summary_md": "**KO Dividend Safety** | Verdict: VERY_LOW_RISK (Score: 88.5/100) | Yield: 2.25% (5Y Avg: 2.83%) | Payout: 61.95% | Rate: $2.12/yr | Ex-Div: 2026-12-01 | OCF Coverage: 1.78x | Type: EQUITY"
}
```

***

### 🛠️ Input Parameters

| Parameter | Type | Default | Description |
| :--- | :--- | :--- | :--- |
| `tickers` | `array` | `["SCHD", "O", "JNJ", "KO", "AAPL", "NVDA"]` | List of US stock and dividend ETF tickers to analyze. |
| `maxItems` | `integer` | `50` | Maximum number of tickers to analyze per run. |

***

### ⚖️ License

Apache 2.0

# Actor input Schema

## `tickers` (type: `array`):

List of US stock and dividend ETF tickers to analyze for dividend yield, payout ratio safety, and cashflow dividend coverage.

## `maxItems` (type: `integer`):

Maximum number of stocks/ETFs to process.

## Actor input object example

```json
{
  "tickers": [
    "SCHD",
    "O",
    "JNJ",
    "KO",
    "AAPL",
    "NVDA"
  ],
  "maxItems": 50
}
```

# Actor output Schema

## `results` (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 = {
    "tickers": [
        "SCHD",
        "O",
        "JNJ",
        "KO",
        "AAPL",
        "NVDA"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("mcp_collector/us-dividend-safety-growth-mcp").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 = { "tickers": [
        "SCHD",
        "O",
        "JNJ",
        "KO",
        "AAPL",
        "NVDA",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("mcp_collector/us-dividend-safety-growth-mcp").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 '{
  "tickers": [
    "SCHD",
    "O",
    "JNJ",
    "KO",
    "AAPL",
    "NVDA"
  ]
}' |
apify call mcp_collector/us-dividend-safety-growth-mcp --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,mcp_collector/us-dividend-safety-growth-mcp"
        }
    }
}
```

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/cayJDut1cGcpt706Q/builds/jGdxQf7V0QHaowgn3/openapi.json