# FDA Debarment Screening — Current Drug & Food Lists (`quartz_apple_dnx/fda-debarment-screening`) Actor

Screen person and firm names against FDA's current Drug Applications, Drug Imports, and Food Imports debarment lists using exact normalized matching.

- **URL**: https://apify.com/quartz_apple_dnx/fda-debarment-screening.md
- **Developed by:** [Piven Core](https://apify.com/quartz_apple_dnx) (community)
- **Categories:** Business, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$5.00 / 1,000 fda debarment name-screen 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?

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

## FDA Debarment Screening — Current Drug & Food Lists

Screen person and firm names against FDA's current consolidated debarment workbook.

The Actor checks all three current FDA lists:

- Drug Product Applications
- Drug Imports
- Food Imports

It uses **exact normalized name matching only**. There is no fuzzy risk score, no identity guess, and no automated adverse-decision recommendation.

**Independent tool. Not affiliated with or endorsed by the U.S. Food and Drug Administration (FDA).**

### Why use it

FDA debarment rules can matter in drug-application and import workflows. This Actor turns the current FDA workbook into a simple structured screening step suitable for:

- drug-application vendor/service-provider checks
- import compliance research
- legal/compliance research
- due-diligence workflows
- repeatable audit pipelines

### Try it

```json
{
  "names": [
    "Gina Acosta",
    "Quality Poultry and Seafood Inc.",
    "Piven No Exact Match 987654"
  ],
  "onlyMatches": false
}
```

The production gate rechecks known current FDA fixtures before release.

### Output

Each returned screening row contains:

- submitted name
- normalized comparison key
- `match` or `no-exact-match`
- exact match count
- matching FDA list(s)
- person vs. firm
- FDA source name
- effective date
- debarment term
- Federal Register date/reference
- attached correction/continuation notices when present
- source URL
- workbook freshness metadata
- capture timestamp

FDA correction/continuation rows are attached to the related debarment record rather than incorrectly represented as separate people or firms.

### Matching rules

V1 intentionally uses exact normalized matching.

Normalization handles case, punctuation, whitespace, accents, and person-name order such as:

- `Gina Acosta`
- `Acosta, Gina`

It does **not** perform fuzzy matching or assume that similar names identify the same person.

A returned match is a **name match, not identity confirmation**.

A `no-exact-match` result means only that no exact normalized match was found in the current workbook. It is **not clearance**, proof that a person/entity is not debarred, legal advice, or a substitute for reviewing the official record.

### Matches-only mode

Set `onlyMatches: true` to omit unmatched screening rows.

If none of the submitted names match, the Actor writes one truthful non-billed summary row.

### Pricing

Private candidate pricing: **$0.005 per returned screening-result row ($5 per 1,000)**.

The price is subject to the live economics gate and final Store market recheck before publication.

Only returned screening-result rows are billed. Summary rows are not billed.

### Official source

FDA Debarment List Updates:

`https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/fda-debarment-list-drug-product-applications/fda-debarment-list-updates`

Current consolidated workbook:

`https://www.fda.gov/media/147870/download?attachment=`

The workbook currently contains separate sheets for Drug Applications, Drug Imports, and Food Imports.

### Important limitations

This tool is a factual FDA-list screening aid. It must not be used as an automated employment, credit, housing, insurance, or other high-impact eligibility decision system.

Before acting on a match, verify identity and review the official FDA and Federal Register records. Names can be shared by multiple people or organizations, and source records can change.

### About Piven Core

Built and maintained by **Piven Core** — focused data APIs and automation utilities for reliable, production-ready workflows.

Learn more: https://pivencore.com

# Actor input Schema

## `names` (type: `array`):

Person or firm names. Exact normalized matching only; no fuzzy identity inference.

## `onlyMatches` (type: `boolean`):

If enabled, unmatched input names are omitted. If none match, the Actor writes one non-billed summary row.

## Actor input object example

```json
{
  "names": [
    "Gina Acosta",
    "Quality Poultry and Seafood Inc.",
    "Piven No Exact Match 987654"
  ],
  "onlyMatches": false
}
```

# Actor output Schema

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

No description

## `summary` (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 = {
    "names": [
        "Gina Acosta",
        "Quality Poultry and Seafood Inc.",
        "Piven No Exact Match 987654"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("quartz_apple_dnx/fda-debarment-screening").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 = { "names": [
        "Gina Acosta",
        "Quality Poultry and Seafood Inc.",
        "Piven No Exact Match 987654",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("quartz_apple_dnx/fda-debarment-screening").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 '{
  "names": [
    "Gina Acosta",
    "Quality Poultry and Seafood Inc.",
    "Piven No Exact Match 987654"
  ]
}' |
apify call quartz_apple_dnx/fda-debarment-screening --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,quartz_apple_dnx/fda-debarment-screening"
        }
    }
}
```

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/wmseYvT0pnQlLw7Ul/builds/TtPvS1ahaVQNDB39t/openapi.json