# PDF Text Extractor — tables, text and data to clean JSON (`amanatools/pdf-table-extractor`) Actor

Extract every table from PDF files into clean, header-mapped JSON rows. Built for AI agents, data pipelines, and spreadsheet workflows. Pay per document processed.

- **URL**: https://apify.com/amanatools/pdf-table-extractor.md
- **Developed by:** [Dos](https://apify.com/amanatools) (community)
- **Categories:** Developer tools, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% 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

## PDF Text Extractor — tables, text and data to clean JSON

Extract text and **tables** from PDF files by URL. Feed it PDF links, get back **every table as clean, header-mapped JSON rows** — ready for spreadsheets, databases, LLM pipelines, and downstream agents. No OCR gimmicks, no layout guessing you have to clean up afterward: headers are detected, duplicate column names are de-duplicated, whitespace is normalized, and empty rows are dropped.

### What it does

- Downloads each PDF (up to 100 per run, 50 MB each)
- Scans the pages you choose (`all`, `1-5`, or `1,3,7-9`)
- Extracts every table on those pages
- Maps each table to `{headers, rows}` where `rows` is a list of `{column: value}` objects
- Outputs one dataset item per document: `{url, status, n_tables, tables[]}`

### Why agents use it

Financial reports, invoices, government publications, price lists, timetables — the world's structured data ships inside PDFs. This actor turns that into JSON your workflow can actually use, in one call. Works as an MCP tool out of the box.

### Input example

```
{
    "pdf_urls": ["https://example.com/report.pdf"],
    "pages": "all"
}
```

### Pricing

Pay-per-event: a small fee per document processed plus a micro-fee per table extracted. No subscription, no minimum.

### Notes & limits

- Text-based PDFs only (scanned/image PDFs need OCR — not included in v0.1; tell us if you need it and it becomes v0.2)
- Documents that fail or contain no tables are reported with `status` so your pipeline can branch cleanly

### Other actors by amanatools

- [PDF OCR Extractor](https://apify.com/amanatools/pdf-ocr-extractor) — scanned and image-only PDFs into searchable text
- [ATS Job Scraper](https://apify.com/amanatools/ats-jobs-extractor) — open jobs straight from Greenhouse, Lever, Workday and more
- [Data Cleaner](https://apify.com/amanatools/data-cleaner) — messy CSV, Excel and JSON into clean, typed data
- [Doc to Markdown](https://apify.com/amanatools/doc-to-markdown) — DOCX, PDF and web pages into clean markdown

# Actor input Schema

## `pdf_urls` (type: `array`):

Direct links to the PDF files to process (up to 100 per run).

## `pages` (type: `string`):

Which pages to scan: 'all', a range like '1-5', or a list like '1,3,7-9'.

## `include_documents_without_tables` (type: `boolean`):

If enabled, documents that fail or contain no tables still appear in the dataset with their status.

## Actor input object example

```json
{
  "pdf_urls": [
    "https://raw.githubusercontent.com/jsvine/pdfplumber/stable/examples/pdfs/ca-warn-report.pdf"
  ],
  "pages": "all",
  "include_documents_without_tables": true
}
```

# Actor output Schema

## `results` (type: `string`):

Default dataset: one item per PDF with url, status, n\_tables, error, and the tables array (page, table\_index, headers, rows, n\_rows, n\_cols).

# 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 = {
    "pdf_urls": [
        "https://raw.githubusercontent.com/jsvine/pdfplumber/stable/examples/pdfs/ca-warn-report.pdf"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("amanatools/pdf-table-extractor").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 = { "pdf_urls": ["https://raw.githubusercontent.com/jsvine/pdfplumber/stable/examples/pdfs/ca-warn-report.pdf"] }

# Run the Actor and wait for it to finish
run = client.actor("amanatools/pdf-table-extractor").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 '{
  "pdf_urls": [
    "https://raw.githubusercontent.com/jsvine/pdfplumber/stable/examples/pdfs/ca-warn-report.pdf"
  ]
}' |
apify call amanatools/pdf-table-extractor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=amanatools/pdf-table-extractor",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/2iDsbnaBbdkmvWQww/builds/iyMT10CWhwsetrnLt/openapi.json