# Apify Market Gap Finder (`zinin/apify-market-gap-finder`) Actor

Compare user-declared niches against the public Apify Store and rank evidence-backed market gaps. Public Store API only, no LLM.

- **URL**: https://apify.com/zinin/apify-market-gap-finder.md
- **Developed by:** [Tim Zinin](https://apify.com/zinin) (community)
- **Categories:** MCP servers, Lead generation
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per event

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/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

## Apify Market Gap Finder

Compare user-declared niches against the public Apify Store and rank evidence-backed
market gaps. The Actor uses only `GET https://api.apify.com/v2/store`.
It returns transparent cohort metrics, confidence, and ranked opportunities without inventing market-size claims.

### What you get

- Scores each seed query against an isolated public Store cohort.
- Ranks the niches by the shared Wave 3 opportunity formula.
- Returns one dataset report row with scored opportunities and source metrics.
- Writes the compact run summary to default KVS key `OUTPUT`.

### How to use

1. Click **Try for free** and enter at least two niche queries.
2. Set a unique `requestId` and optionally choose Store categories.
3. Start the run and read ranked opportunities from the dataset row.

### Pricing

Pay-per-event:

- `apify-actor-start`: $0.005
- `result-found`: $0.10

### Input

```json
{
  "schemaVersion": "1.0",
  "requestId": "gap-001",
  "seedQueries": ["market gap", "pricing gap"],
  "categories": ["LEAD_GENERATION"],
  "maxResults": 10,
  "freshnessMinutes": 60,
  "detailLevel": "compact"
}
```

### Output

```json
{
  "schemaVersion": "1.0",
  "requestId": "gap-001",
  "reportType": "market_gap_report",
  "generatedAt": "2026-07-31T00:00:00.000Z",
  "sampleConfidence": "low",
  "evaluatedQueryCount": 2,
  "opportunities": [
    {
      "rank": 1,
      "niche": "market gap",
      "opportunity": { "score": 61.2 }
    }
  ]
}
```

Missing fields stay null. No revenue, profit, or private-account data is inferred.
The business envelope is written to KVS key `OUTPUT`; the same `requestId` is a
replay and cannot create another Dataset row or `result-found` charge.

#### Related tools

Related tools for adjacent workflows in in-house factory tooling.

| Actor | What it does |
|---|---|
| [Apify Actor Idea Validator](https://apify.com/zinin/apify-actor-idea-validator) | Pair it in the in-house factory tooling workflow: Assess one proposed Apify Actor idea against public Store similarity, activity, quality, competition and... |
| [Apify Niche Demand Radar](https://apify.com/zinin/apify-niche-demand-radar) | Pair it in the in-house factory tooling workflow: Produce a current demand-direction snapshot for one declared niche using observed public Apify Store user... |
| [Apify Portfolio Expansion Planner](https://apify.com/zinin/apify-portfolio-expansion-planner) | Pair it in the in-house factory tooling workflow: Rank user-supplied candidate niches by public Store opportunity and textual adjacency to a user-supplied... |
| [Apify Pricing Benchmark Advisor](https://apify.com/zinin/apify-pricing-benchmark-advisor) | Pair it in the in-house factory tooling workflow: Summarize visible public Apify Store event-price distributions for a declared cohort |

### FAQ / Support

**Does a small sample prove a niche is empty?** No. Missing fields and limited cohorts remain explicit in the report.

**What this is NOT.** It is not a market-size, revenue, or autonomous niche-invention tool. Report bugs through the Actor page.

# Actor input Schema

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

Contract version for the business input.

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

Idempotency key for replay/conflict handling.

## `maxResults` (type: `integer`):

Maximum number of matched actors to keep in the detailed report.

## `freshnessMinutes` (type: `integer`):

Age limit for report-cache reuse. Set to 0 to bypass cache hits.

## `detailLevel` (type: `string`):

Compact or evidence-heavy output in the dataset row.

## `seedQueries` (type: `array`):

User-declared niche queries to score independently.

## `categories` (type: `array`):

Optional Store categories to filter each niche cohort.

## Actor input object example

```json
{
  "schemaVersion": "1.0",
  "requestId": "gap-daily-prefill-001",
  "maxResults": 10,
  "freshnessMinutes": 60,
  "detailLevel": "compact",
  "seedQueries": [
    "market gap",
    "pricing gap"
  ],
  "categories": [
    "LEAD_GENERATION"
  ]
}
```

# Actor output Schema

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

Compact business OUTPUT envelope stored in the default key-value store.

# 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": "gap-daily-prefill-001",
    "maxResults": 10,
    "freshnessMinutes": 60,
    "detailLevel": "compact",
    "seedQueries": [
        "market gap",
        "pricing gap"
    ],
    "categories": [
        "LEAD_GENERATION"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("zinin/apify-market-gap-finder").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": "gap-daily-prefill-001",
    "maxResults": 10,
    "freshnessMinutes": 60,
    "detailLevel": "compact",
    "seedQueries": [
        "market gap",
        "pricing gap",
    ],
    "categories": ["LEAD_GENERATION"],
}

# Run the Actor and wait for it to finish
run = client.actor("zinin/apify-market-gap-finder").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).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": "gap-daily-prefill-001",
  "maxResults": 10,
  "freshnessMinutes": 60,
  "detailLevel": "compact",
  "seedQueries": [
    "market gap",
    "pricing gap"
  ],
  "categories": [
    "LEAD_GENERATION"
  ]
}' |
apify call zinin/apify-market-gap-finder --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=zinin/apify-market-gap-finder",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/gpH6ZPIGjV3QWLAAv/builds/8rmyCHxLPtG8aMkdh/openapi.json