# Paperform Discovery Spider (`getdataforme/paperform-discovery-spider`) Actor

The Paperform Discovery Spider automates data extraction from Paperform sites using customizable queries. It offers high-quality, structured data output in JSON, CSV, or Excel formats. Ideal for research, market analysis, and business automation, it ensures efficient, scalable data collection.

- **URL**: https://apify.com/getdataforme/paperform-discovery-spider.md
- **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community)
- **Categories:** AI, Automation, E-commerce
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $9.00 / 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?

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

## Paperform Discovery Spider

### Introduction

The Paperform Discovery Spider is designed to efficiently crawl and extract data from Paperform sites based on specified queries. This actor is ideal for users seeking to automate data collection, streamline research processes, and gain insights from Paperform templates.

### Features

- **Automated Data Extraction**: Seamlessly gather information from Paperform sites using predefined queries.
- **High Data Quality**: Ensures accurate and comprehensive data collection with structured output.
- **Performance Efficiency**: Optimized for quick and reliable data retrieval.
- **Customizable Queries**: Tailor the spider's search parameters to fit specific research needs.
- **Versatile Output Formats**: Export data in JSON, CSV, or Excel formats for easy integration.
- **User-Friendly Interface**: Simple setup and execution process for hassle-free operation.
- **Scalable Solution**: Suitable for both small-scale and large-scale data collection tasks.

### Input Parameters

| Parameter | Type   | Required | Description                                      | Example        |
|-----------|--------|----------|--------------------------------------------------|----------------|
| Queries   | Array  | No       | The queries for the spider.                      | `["site"]`     |

### Example Usage

#### Example Input JSON

```json
{
  "Queries": ["site"]
}
```

#### Example Output JSON

```json
[
  {
    "name": "Site Emergency Eyewash and Shower Accessibility Audit",
    "short_description": "Comprehensive safety inspection form to verify emergency eyewash stations and safety showers meet ANSI Z358.1 standards for accessibility, 10-second reach time, clear pathways, and proper signage.",
    "meta_title": "Emergency Eyewash and Shower Accessibility Audit Form Template",
    "meta_description": "Professional safety inspection form for verifying emergency eyewash stations and safety showers meet ANSI Z358.1 standards with 10-second reach time, clear pathways, and proper signage compliance.",
    "categories": ["construction", "reporting"],
    "subcategories": ["safety-inspections", "incident-reports", "inspection-checklists", "safety-checklists"],
    "industries": ["ind-construction", "ind-manufacturing", "ind-trades-contractors", "ind-healthcare", "ind-environmental"],
    "professions": ["prof-safety-officer", "prof-facilities-manager", "prof-manager", "prof-contractor", "prof-engineer", "prof-building-inspector"],
    "popularity": 0,
    "created_at": "2026-02-05T23:31:24.505Z",
    "updated_at": "2026-02-05T23:31:24.505Z",
    "status": "active",
    "slug": "site-emergency-eyewash-and-shower-accessibility-audit",
    "actor_id": "qhXTMa640GWksg8SI",
    "run_id": "NNWtIbmjtUnkCHhoW"
  }
]
```

### Use Cases

- **Market Research and Analysis**: Gather data on industry trends and consumer preferences.
- **Competitive Intelligence**: Monitor competitor offerings and strategies.
- **Price Monitoring**: Track pricing changes across various Paperform templates.
- **Content Aggregation**: Compile information for content creation and curation.
- **Academic Research**: Collect data for scholarly studies and publications.
- **Business Automation**: Streamline data collection processes for operational efficiency.

### Installation and Usage

1. Search for "Paperform Discovery Spider" in the Apify Store.
2. Click "Try for free" or "Run".
3. Configure input parameters.
4. Click "Start" to begin extraction.
5. Monitor progress in the log.
6. Export results in your preferred format (JSON, CSV, Excel).

### Output Format

The output data is structured in JSON format, containing fields such as `name`, `short_description`, `meta_title`, `meta_description`, `categories`, `subcategories`, `industries`, `professions`, `popularity`, `created_at`, `updated_at`, `status`, `slug`, `actor_id`, and `run_id`. Each field provides specific insights into the Paperform template's characteristics and metadata.

### Support

For custom/simplified outputs or bug reports, please contact:

- Email: support@getdataforme.com
- Subject line: "custom support"
- Contact form: <https://getdataforme.com/contact/>

We're here to help you get the most out of this Actor!

# Actor input Schema

## `Queries` (type: `array`):

The queries for the spider.

## Actor input object example

```json
{
  "Queries": [
    "site"
  ]
}
```

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("getdataforme/paperform-discovery-spider").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("getdataforme/paperform-discovery-spider").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 '{}' |
apify call getdataforme/paperform-discovery-spider --silent --output-dataset

```

## MCP server setup

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

```

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/qhXTMa640GWksg8SI/builds/P9Wn6x8FmJkNisG2c/openapi.json