# SEC EDGAR Scraper (`labrat011/sec-edgar-scraper`) Actor

Resolve companies to SEC CIK numbers, search SEC filings, and extract structured XBRL financial facts from EDGAR. No API key required.

- **URL**: https://apify.com/labrat011/sec-edgar-scraper.md
- **Developed by:** [mick\_](https://apify.com/labrat011) (community)
- **Categories:** Lead generation, MCP servers
- **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.50 / 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

## SEC EDGAR Entity Resolver

Resolve companies to SEC CIK numbers, search SEC filings, and extract structured XBRL financial facts from EDGAR. No API key required. MCP-ready for AI agent integration.

### What does it do?

SEC EDGAR Entity Resolver turns company names, stock tickers, and CIK numbers into structured SEC data. It queries the SEC's public EDGAR APIs and returns clean, normalized results ready for analysis, compliance workflows, or AI agent consumption via MCP.

**Use cases:**

- **Entity resolution** -- ground company names to official SEC identifiers (CIK numbers) for downstream data pipelines
- **Due diligence** -- pull company profiles with SIC codes, addresses, fiscal year end, and filing history
- **Compliance monitoring** -- search SEC filings by form type, date range, and keyword to track regulatory submissions
- **Financial analysis** -- extract structured XBRL facts (revenue, assets, liabilities) from company filings
- **AI agent tooling** -- expose as an MCP tool so AI agents can look up SEC data in real time
- **Investment research** -- monitor 10-K, 10-Q, 8-K, and other filings for companies or industries

### Features

- **3 modes:** `resolve_entity`, `search_filings`, `get_company_facts`
- **No API key required** -- all data comes from public SEC EDGAR endpoints
- **No proxies needed** -- direct API access to government infrastructure
- **Ticker preload** -- loads SEC `company_tickers.json` for fast ticker-to-CIK resolution
- **Polite rate limiting** -- default 0.3s between requests; retry with exponential backoff on 429/5xx
- **State persistence** -- survives Apify actor migrations mid-run
- **Batch push** -- outputs in batches of 25 for efficiency
- **Free tier** -- 25 results per run without a subscription

### What data does it extract?

#### Entities (resolve\_entity)

| Field | Description |
|-------|-------------|
| `schema_version` | Schema version (currently `"1.0"`) |
| `type` | Always `"entity"` |
| `cik` | 10-digit SEC CIK number |
| `name` | Official company name |
| `tickers` | Stock ticker symbols |
| `sic` | SIC industry code |
| `sic_description` | SIC industry description |
| `state` | State of incorporation |
| `country` | Country of incorporation |
| `fiscal_year_end` | Fiscal year end (MMDD) |
| `mailing_address` | Mailing address |
| `business_address` | Business address |
| `former_names` | Previous company names |
| `recent_filings` | Recent SEC filings (form, date, accession number, URLs) |
| `source_urls` | SEC API URLs used |

#### Filings (search\_filings)

| Field | Description |
|-------|-------------|
| `schema_version` | Schema version (currently `"1.0"`) |
| `type` | Always `"filing"` |
| `cik` | Filer CIK number |
| `name` | Filer name |
| `tickers` | Filer ticker symbols |
| `form` | Form type (10-K, 10-Q, 8-K, etc.) |
| `file_date` | Filing date |
| `acceptance_datetime` | SEC acceptance timestamp |
| `accession_number` | Unique filing accession number |
| `filing_url` | Link to filing index page |
| `primary_document_url` | Link to primary document |
| `items` | Filing items (for 8-K) |
| `state` | Filer state |
| `sic` | Filer SIC code |
| `sic_description` | Filer SIC description |

#### Facts (get\_company\_facts)

| Field | Description |
|-------|-------------|
| `schema_version` | Schema version (currently `"1.0"`) |
| `type` | Always `"fact"` |
| `cik` | Company CIK number |
| `name` | Company name |
| `sic` | SIC code |
| `concept` | XBRL concept name (e.g., `Revenue`, `Assets`) |
| `namespace` | XBRL namespace (e.g., `us-gaap`) |
| `label` | Human-readable concept label |
| `unit` | Unit of measurement (e.g., `USD`, `shares`) |
| `value` | Reported value |
| `start` | Period start date (duration facts) |
| `end` | Period end date |
| `form` | Source form type |
| `frame` | XBRL reporting frame (e.g., `CY2025Q1`) |
| `filing_url` | Link to source filing |

***

### 👥 Who Uses This

#### 📈 Financial Analysts & Investment Researchers

The most common workflow: pull annual reports for a company, compare performance across competitors, track how a metric has changed quarter over quarter. All three modes serve this directly.

- Resolve a company name or ticker to its CIK, then pull recent 10-K and 10-Q filings with direct URLs to the primary documents
- Search across an industry by keyword to find all companies disclosing a specific risk factor or business change
- Compare revenue, gross profit, and operating income across multiple companies using `get_company_facts` with specific XBRL concepts

```json
{
    "mode": "search_filings",
    "query": "artificial intelligence revenue",
    "formTypes": ["10-K"],
    "dateFrom": "2024-01-01",
    "dateTo": "2026-01-01",
    "maxResults": 100,
    "userAgent": "YourName your-email@example.com"
}
```

#### 📊 Quant Traders & Hedge Funds

XBRL financial facts are machine-readable, time-stamped, and directly comparable across companies. The `get_company_facts` mode returns structured time series — no PDF parsing required.

- Pull quarterly Revenue, Assets, and Liabilities time series for a company going back years
- Extract EPS, shares outstanding, or debt-to-equity ratios as structured data for modeling
- Cross-reference 8-K material event filings against price movement data for event-driven strategies

```json
{
    "mode": "get_company_facts",
    "ticker": "NVDA",
    "namespaces": ["us-gaap"],
    "concepts": ["Revenues", "Assets", "LiabilitiesAndStockholdersEquity"],
    "maxResults": 500,
    "userAgent": "YourName your-email@example.com"
}
```

#### ⚖️ Compliance & Legal Teams

Monitoring companies for material events, regulatory disclosures, and insider transactions requires catching 8-K and other filings as they land. This actor can be scheduled to run daily.

- Monitor a specific company for all 8-K material event filings — acquisitions, leadership changes, earnings, legal proceedings
- Search proxy statements (DEF 14A) and annual reports for specific disclosure language
- Track amendment filings (10-K/A, 8-K/A) to identify when companies are restating prior disclosures

```json
{
    "mode": "search_filings",
    "ticker": "META",
    "formTypes": ["8-K", "8-K/A"],
    "dateFrom": "2025-01-01",
    "maxResults": 200,
    "userAgent": "YourName your-email@example.com"
}
```

#### 🗞️ Investigative Journalists

Full-text keyword search across all SEC filings is one of EDGAR's most powerful but least accessible features. This actor exposes it as a simple JSON call.

- Search for a specific phrase across all public SEC filings to find which companies mentioned it
- Track disclosures around a breaking story — find every company that mentioned a specific risk, event, or name
- Pull a company's full filing history to trace a timeline of disclosures around a specific issue

```json
{
    "mode": "search_filings",
    "query": "climate transition risk stranded assets",
    "formTypes": ["10-K"],
    "dateFrom": "2023-01-01",
    "maxResults": 200,
    "userAgent": "YourName your-email@example.com"
}
```

#### 🤖 AI/LLM Engineers

SEC filings are dense, authoritative, and structured — ideal source material for financial AI agents and RAG pipelines. The entity resolution mode is especially useful as a first step in any company-focused pipeline.

- Resolve a company name to its CIK programmatically — necessary before any downstream EDGAR API calls
- Use via MCP so AI agents can look up company filings in real time from a natural language prompt
- Pull XBRL facts as structured financial context before generating analysis, summaries, or comparison reports

```json
{
    "mode": "resolve_entity",
    "query": "OpenAI",
    "maxRecentFilings": 20,
    "userAgent": "YourName your-email@example.com"
}
```

***

### 🔗 Related Actors

Other actors from the same portfolio that pair well with SEC EDGAR data:

| Actor | What It Does |
|---|---|
| [Government Contracts Scraper](https://apify.com/labrat011/gov-contracts-scraper) | Federal contract awards from SAM.gov — research what government contracts awardees have won |
| [Academic Paper Scraper](https://apify.com/labrat011/academic-paper-scraper) | Research papers from ArXiv and Semantic Scholar — pair with EDGAR for tech company R\&D analysis |
| [Reddit Scraper](https://apify.com/labrat011/reddit-scraper) | Retail investor sentiment from Reddit — cross-reference with SEC filings for market signal research |
| [NPI Provider Contact Finder](https://apify.com/labrat011/npi-provider-contact-finder) | Healthcare provider data — pairs with SEC filings research on pharma and healthcare companies |

***

### Input

#### Mode 1: Resolve Entity

Resolve a company name, ticker, or CIK to a full SEC entity profile with recent filings.

```json
{
    "mode": "resolve_entity",
    "query": "NVIDIA",
    "maxRecentFilings": 10,
    "userAgent": "YourName your-email@example.com"
}
```

Resolve by ticker:

```json
{
    "mode": "resolve_entity",
    "ticker": "AAPL",
    "maxRecentFilings": 5,
    "userAgent": "YourName your-email@example.com"
}
```

#### Mode 2: Search Filings

Search SEC filings by keyword, company, form type, and date range.

```json
{
    "mode": "search_filings",
    "query": "artificial intelligence",
    "formTypes": ["10-K", "10-Q"],
    "dateFrom": "2025-01-01",
    "dateTo": "2026-01-01",
    "maxResults": 100,
    "userAgent": "YourName your-email@example.com"
}
```

Search by ticker and form prefix:

```json
{
    "mode": "search_filings",
    "ticker": "TSLA",
    "formPrefix": "8-",
    "maxResults": 50,
    "userAgent": "YourName your-email@example.com"
}
```

#### Mode 3: Get Company Facts

Pull structured XBRL financial data for a company.

```json
{
    "mode": "get_company_facts",
    "ticker": "MSFT",
    "namespaces": ["us-gaap"],
    "concepts": ["Revenue", "Assets"],
    "maxResults": 200,
    "userAgent": "YourName your-email@example.com"
}
```

#### Input Reference

**Common fields:**

| Parameter | Default | Description |
|-----------|---------|-------------|
| `mode` | `resolve_entity` | `resolve_entity`, `search_filings`, or `get_company_facts` |
| `query` | | Company name, ticker, CIK, or search text |
| `cik` | | 10-digit CIK (zero-padded if shorter) |
| `ticker` | | Stock ticker symbol |
| `userAgent` | | SEC-required User-Agent: `"YourName email@example.com"` |
| `maxResults` | `100` | Max results (1-1000; free tier capped at 25) |
| `requestIntervalSecs` | `0.3` | Seconds between requests |
| `timeoutSecs` | `30` | HTTP timeout |
| `maxRetries` | `5` | Retries on failure |

**resolve\_entity fields:**

| Parameter | Default | Description |
|-----------|---------|-------------|
| `maxRecentFilings` | `10` | Recent filings to include (1-100) |
| `includeAmendments` | `true` | Include amended forms (\*/A) |
| `formPrefix` | | Filter filings by prefix (e.g., `10-`) |

**search\_filings fields:**

| Parameter | Default | Description |
|-----------|---------|-------------|
| `formTypes` | | List of forms: `10-K`, `10-Q`, `8-K`, etc. |
| `formPrefix` | | Prefix filter (e.g., `10-` matches 10-K, 10-Q) |
| `includeAmendments` | `true` | Include amended forms (\*/A) |
| `dateFrom` | | Filed on or after (YYYY-MM-DD) |
| `dateTo` | | Filed on or before (YYYY-MM-DD) |
| `start` | `0` | Pagination offset |

**get\_company\_facts fields:**

| Parameter | Default | Description |
|-----------|---------|-------------|
| `namespaces` | | XBRL namespaces (e.g., `us-gaap`, `dei`) |
| `concepts` | | Concept names (e.g., `Revenue`, `Assets`) |
| `periodType` | | `instant` or `duration` |

***

### Output

Results are saved to the default dataset. Download them in JSON, CSV, Excel, or XML from the Output tab.

#### Example: Entity output

```json
{
    "schema_version": "1.0",
    "type": "entity",
    "cik": "0001045810",
    "name": "NVIDIA CORP",
    "tickers": ["NVDA"],
    "sic": "3674",
    "sic_description": "Semiconductors & Related Devices",
    "state": "DE",
    "country": "US",
    "fiscal_year_end": "0128",
    "mailing_address": {"street1": "2788 SAN TOMAS EXPRESSWAY", "city": "SANTA CLARA", "state": "CA", "zip": "95051"},
    "business_address": {"street1": "2788 SAN TOMAS EXPRESSWAY", "city": "SANTA CLARA", "state": "CA", "zip": "95051"},
    "former_names": [],
    "recent_filings": [
        {
            "form": "10-K",
            "fileDate": "2026-02-01",
            "accessionNumber": "0001045810-26-000123",
            "filingUrl": "https://www.sec.gov/Archives/edgar/data/...",
            "primaryDocumentUrl": "https://www.sec.gov/Archives/edgar/data/..."
        }
    ],
    "source_urls": ["https://data.sec.gov/submissions/CIK0001045810.json"]
}
```

#### Example: Filing output

```json
{
    "schema_version": "1.0",
    "type": "filing",
    "cik": "0001318605",
    "name": "TESLA INC",
    "tickers": ["TSLA"],
    "form": "8-K",
    "file_date": "2026-01-15",
    "acceptance_datetime": "2026-01-15T16:30:00.000Z",
    "accession_number": "0001318605-26-000456",
    "filing_url": "https://www.sec.gov/Archives/edgar/data/...",
    "primary_document_url": "https://www.sec.gov/Archives/edgar/data/...",
    "items": ["2.02", "9.01"],
    "state": "TX",
    "sic": "3711",
    "sic_description": "Motor Vehicles & Passenger Car Bodies"
}
```

#### Example: Fact output

```json
{
    "schema_version": "1.0",
    "type": "fact",
    "cik": "0000789019",
    "name": "MICROSOFT CORP",
    "sic": "7372",
    "concept": "Revenue",
    "namespace": "us-gaap",
    "label": "Revenue from Contract with Customer, Excluding Assessed Tax",
    "unit": "USD",
    "value": 56189000000,
    "start": "2025-07-01",
    "end": "2025-09-30",
    "form": "10-Q",
    "frame": "CY2025Q3",
    "filing_url": "https://www.sec.gov/Archives/edgar/data/..."
}
```

***

### Cost

This actor uses **pay-per-event (PPE) pricing**. You pay only for the results you get.

- **$0.50 per 1,000 results** ($0.0005 per result)
- **No proxy costs** -- public government APIs
- **No API key costs** -- SEC EDGAR is free and public
- Free tier: **25 results per run** (no subscription required)

***

### Technical details

- SEC EDGAR EFTS API (`efts.sec.gov/LATEST/search-index`) for full-text filing search
- SEC EDGAR Submissions API (`data.sec.gov/submissions/`) for entity profiles and recent filings
- SEC EDGAR XBRL CompanyFacts API (`data.sec.gov/api/xbrl/companyfacts/`) for structured financial data
- SEC company tickers file (`sec.gov/files/company_tickers.json`) preloaded for fast ticker resolution
- SEC requires a `User-Agent` header with name and email on all requests
- Rate limited to 1 request per 0.3 seconds (configurable)
- Automatic retry with exponential backoff and jitter on failures
- Results pushed in batches of 25 for efficiency
- Actor state persisted across migrations
- No proxies, no browser, no cookies -- direct API access

***

### MCP Integration

This actor works as an MCP tool through Apify's hosted MCP server. No custom server needed.

- **Endpoint:** `https://mcp.apify.com?tools=labrat011/sec-edgar-scraper`
- **Auth:** `Authorization: Bearer <APIFY_TOKEN>`
- **Transport:** Streamable HTTP
- **Works with:** Claude Desktop, Cursor, VS Code, Windsurf, Warp, Gemini CLI

**Example MCP config (Claude Desktop / Cursor):**

```json
{
    "mcpServers": {
        "sec-edgar-scraper": {
            "url": "https://mcp.apify.com/?tools=labrat011/sec-edgar-scraper",
            "headers": {
                "Authorization": "Bearer <APIFY_TOKEN>"
            }
        }
    }
}
```

AI agents can use this actor to resolve company names to CIK numbers, search SEC filings, and pull structured XBRL financial data -- all as a callable MCP tool.

***

### FAQ

#### Do I need an API key?

No. SEC EDGAR is a public government data source with no authentication. You do need to provide a `User-Agent` header (name and email) per SEC's fair access policy.

#### What is a CIK?

A Central Index Key (CIK) is the SEC's unique identifier for every entity that files with the commission. Think of it as an SEC serial number for companies.

#### Can I combine filters?

Yes. For filing search, combine keyword + form types + date range + ticker/CIK. All filters are AND-combined.

#### What XBRL namespaces are available?

The most common are `us-gaap` (US financial reporting standards) and `dei` (document and entity information). Some companies also report under `ifrs-full` (international standards).

***

### Feedback

Found a bug or have a feature request? Open an issue on the actor's Issues tab in Apify Console.

# Actor input Schema

## `mode` (type: `string`):

Select what to do: resolve a company entity, search SEC filings, or get structured XBRL financial facts.

## `query` (type: `string`):

Company name, ticker, or keyword. Used for entity resolution and filing search.

## `cik` (type: `string`):

SEC Central Index Key (e.g., 0001045810). Zero-padded to 10 digits automatically.

## `ticker` (type: `string`):

Stock ticker symbol (e.g., NVDA, AAPL, TSLA). Resolved to CIK via SEC ticker list.

## `formTypes` (type: `array`):

SEC form types to include (e.g., 10-K, 10-Q, 8-K, S-1). Leave empty for all forms.

## `dateFrom` (type: `string`):

Only include filings filed on or after this date (YYYY-MM-DD).

## `dateTo` (type: `string`):

Only include filings filed on or before this date (YYYY-MM-DD).

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

Maximum number of results to return (1-1000). Free users are limited to 25 per run.

## `start` (type: `integer`):

Pagination offset for search\_filings. Use with maxResults to page through large result sets.

## `maxRecentFilings` (type: `integer`):

Number of recent filings to include in resolve\_entity output (1-100).

## `includeAmendments` (type: `boolean`):

Include amended filings (forms ending in /A such as 10-K/A). Set to false to exclude amendments.

## `formPrefix` (type: `string`):

Filter by form prefix (e.g., '10-' matches 10-K, 10-Q, 10-K/A).

## `namespaces` (type: `array`):

Filter facts by XBRL namespace (e.g., us-gaap, dei, ifrs-full). Leave empty for all.

## `concepts` (type: `array`):

Filter facts by concept name (e.g., Revenue, Assets, NetIncomeLoss). Leave empty for all.

## `periodType` (type: `string`):

Filter facts by period type: 'instant' (balance sheet) or 'duration' (income statement).

## `userAgent` (type: `string`):

SEC requires a User-Agent header on all requests. Format: 'YourName your-email@example.com'.

## `requestIntervalSecs` (type: `number`):

Minimum seconds between requests to SEC. Lower values are faster but risk rate limiting.

## `timeoutSecs` (type: `integer`):

Timeout for each HTTP request to SEC EDGAR.

## `maxRetries` (type: `integer`):

Maximum retry attempts on failed or rate-limited requests.

## Actor input object example

```json
{
  "mode": "resolve_entity",
  "query": "NVIDIA",
  "maxResults": 10,
  "start": 0,
  "maxRecentFilings": 10,
  "includeAmendments": true,
  "requestIntervalSecs": 0.3,
  "timeoutSecs": 30,
  "maxRetries": 5
}
```

# 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 = {
    "query": "NVIDIA",
    "maxResults": 10
};

// Run the Actor and wait for it to finish
const run = await client.actor("labrat011/sec-edgar-scraper").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 = {
    "query": "NVIDIA",
    "maxResults": 10,
}

# Run the Actor and wait for it to finish
run = client.actor("labrat011/sec-edgar-scraper").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 '{
  "query": "NVIDIA",
  "maxResults": 10
}' |
apify call labrat011/sec-edgar-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,labrat011/sec-edgar-scraper"
        }
    }
}

```

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/uV75AgWrU6qdu8A0C/builds/ebdt1UUseT7VDOenu/openapi.json