# Us Short Squeeze Sentiment Mcp (`mcp_collector/us-short-squeeze-sentiment-mcp`) Actor

- **URL**: https://apify.com/mcp_collector/us-short-squeeze-sentiment-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 short squeeze 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 Short Squeeze & Short Interest Scanner MCP

[![Apify Actor](https://img.shields.io/badge/Apify-Actor-orange?logo=apify)](https://apify.com)
[![Pricing](https://img.shields.io/badge/Price-%240.008%20%2F%20call-green)](https://apify.com)
[![Runtime](https://img.shields.io/badge/Latency-%3C3s-blue)](https://apify.com)

**Ultra-fast (<3s) Zero-Proxy MCP Tool for AI Agents (Claude, Cursor, LangChain).**\
Analyzes short percent of float, days to cover (short ratio), Month-over-Month short accumulation, and computes a quantitative **Short Squeeze Score (0-100)** with Squeeze Risk Classification in compact JSON and a 1-line Markdown summary.

***

### ⚡ Why AI Agents Need This MCP

1. **Detects Asymmetric Short Squeeze Primers**: High short interest combined with high Days to Cover creates explosive powder-keg conditions where sudden buying pressure forces aggressive short covering.
2. **Downside Risk Protection**: Warns long-only trading agents when massive institutional short positions are aggressively accumulating (+20% MoM), helping avoid catastrophic traps.
3. **95% LLM Context Token Saving**: Raw FINRA/exchange short interest data is scattered and verbose. This tool consolidates everything into a clean <250-token payload.
4. **Pay-Per-Event Micro-Pricing**: Only $0.008 per stock analyzed ($8.00 / 1,000 queries).

***

### 🎯 The Complete 6-Step 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. **Short Squeeze Check**: Call `us-short-squeeze-sentiment-mcp` (this tool) ➔ Check short float %, days to cover, and squeeze score.
5. **Dividend Safety Check**: Call `us-dividend-safety-growth-mcp` ➔ Verify payout safety and cash flow coverage.
6. **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": {
    "short-squeeze": {
      "command": "npx",
      "args": [
        "-y",
        "@apify/mcp-server",
        "--actor",
        "YOUR_APIFY_USERNAME/us-short-squeeze-sentiment-mcp"
      ],
      "env": {
        "APIFY_TOKEN": "YOUR_APIFY_API_TOKEN"
      }
    }
  }
}
```

***

### 📦 Output Sample

```json
{
  "ticker": "GME",
  "company_name": "GameStop Corporation",
  "analyzed_at_kst": "2026-10-31 20:10:00 KST",
  "timestamp_utc": "2026-10-31 11:10:00 UTC",
  "current_price": 24.50,
  "squeeze_risk_level": "HIGH_SQUEEZE_POTENTIAL (높은 스퀴즈 잠재력: 호재 발생 시 가파른 커버링 반등 구간)",
  "short_squeeze_score": 68.5,
  "short_interest_data": {
    "short_percent_of_float": 10.32,
    "short_percent_of_shares_out": 9.52,
    "days_to_cover_short_ratio": 4.33,
    "shares_short": 48031618,
    "shares_short_prior_month": 39069126,
    "short_change_mom_percent": 22.94,
    "short_trend_status": "MASSIVE_ACCUMULATION (전월 대비 공매도 +20% 이상 급증, 숏 포지션 과열)",
    "short_dollar_volume_usd": 1176774641.0,
    "settlement_date": "2026-10-15"
  },
  "share_structure": {
    "float_shares": 460412648,
    "shares_outstanding": 504500990
  },
  "score_breakdown": {
    "short_percent_of_float_score_weight_40": 20.8,
    "days_to_cover_score_weight_35": 22.7,
    "mom_trend_score_weight_25": 25.0,
    "total_short_squeeze_score": 68.5
  },
  "summary_md": "**GME Short Squeeze** | Squeeze Score: 68.5/100 (HIGH_SQUEEZE_POTENTIAL) | Short Float: 10.32% | Days to Cover: 4.33d | MoM Δ: +22.94% | Short Shares: 48,031,618 ($1.18B) | Settlement Date: 2026-10-15"
}
```

***

### 🛠️ Input Parameters

| Parameter | Type | Default | Description |
| :--- | :--- | :--- | :--- |
| `tickers` | `array` | `["GME", "AMC", "CVNA", "TSLA", "NVDA"]` | List of US stock 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 tickers to analyze for short percent of float, days to cover, and short squeeze risk.

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

Maximum number of stocks to process.

## Actor input object example

```json
{
  "tickers": [
    "GME",
    "AMC",
    "CVNA",
    "TSLA",
    "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": [
        "GME",
        "AMC",
        "CVNA",
        "TSLA",
        "NVDA"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("mcp_collector/us-short-squeeze-sentiment-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": [
        "GME",
        "AMC",
        "CVNA",
        "TSLA",
        "NVDA",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("mcp_collector/us-short-squeeze-sentiment-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": [
    "GME",
    "AMC",
    "CVNA",
    "TSLA",
    "NVDA"
  ]
}' |
apify call mcp_collector/us-short-squeeze-sentiment-mcp --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,mcp_collector/us-short-squeeze-sentiment-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/AI0sbb6o1bOW0SABJ/builds/cwDJz1ng9SNtRMcrS/openapi.json