EDGAR Company Facts & Filings for Agents

Fairquery public Apify Store Actor that turns SEC EDGAR public JSON into agent-ready dataset rows for AI agents and MCP tools.

Not investment advice. Output is a structured mirror of public SEC EDGAR data for research and tooling. It is not a recommendation to buy, sell, or hold any security.

When to use this Actor (LLM / tool selection)

Use this Actor when an agent needs:

• Map a ticker, CIK, or company name → official SEC CIK / entity metadata
• Recent SEC filings (10-K, 10-Q, 8-K, …) with document URLs
• XBRL company facts (Assets, NetIncomeLoss, EPS, …) from companyfacts
• Optional plain-text excerpts from primary filing documents

Do not use it for realtime quotes, options chains, insider Form 4 parsing beyond what submissions expose, or non-US registries.

Data sources (SEC fair access)

Resource	URL
Tickers map	https://www.sec.gov/files/company_tickers.json
Submissions	https://data.sec.gov/submissions/CIK##########.json
Company facts	https://data.sec.gov/api/xbrl/companyfacts/CIK##########.json

The Actor sends User-Agent: Fairquery fairquery@proton.me and throttles to ≤ 5 requests/second.

Input

Field	Type	Default	Description
companies	string[]	—	Tickers (AAPL), CIKs (320193), or name fragments (Apple)
mode	filings | facts | both	both	Which row types to emit (always emits company when resolved)
formTypes	string[]	[]	Form filter; empty = all
since	YYYY-MM-DD	—	Keep filings with filingDate >= since
maxFilingsPerCompany	int	10	Cap filing rows per company
metrics	string[]	defaults	XBRL concept names; empty uses a curated us-gaap set
maxFactsPerMetric	int	8	Most-recent values per concept/unit
includeTextExcerpt	bool	false	Fetch primary document text
excerptMaxChars	int	2000	Excerpt length cap

Example input

{
  "companies": ["AAPL", "MSFT"],
  "mode": "both",
  "formTypes": ["10-K", "10-Q"],
  "since": "2023-01-01",
  "maxFilingsPerCompany": 5,
  "metrics": ["Assets", "NetIncomeLoss", "EarningsPerShareDiluted"],
  "includeTextExcerpt": false
}

Output

One dataset row per company, filing, fact, text excerpt, or error. Every row includes:

• schema_version — currently 1.0.0
• type — company | filing | fact | text_excerpt | error
• source_url — SEC URL the row came from
• scraped_at — ISO timestamp

Error rows (machine-readable)

{
  "schema_version": "1.0.0",
  "type": "error",
  "code": "COMPANY_NOT_FOUND",
  "message": "Could not resolve company query: ZZNOTREAL",
  "retryable": false,
  "query": "ZZNOTREAL",
  "source_url": "https://data.sec.gov/"
}

Codes include: INPUT_EMPTY, COMPANY_NOT_FOUND, RESOLVE_FAILED, SUBMISSIONS_FETCH_FAILED, FACTS_FETCH_FAILED, EXCERPT_FETCH_FAILED.

Run summary

Key-value store record RUN_SUMMARY with counts, SEC request total, and echoed input.

Pay-per-event (live)

Users are charged for these events (plus Apify’s default apify-actor-start):

Event	Price	When
apify-actor-start	$0.00005	Once when the run starts (Apify default)
company-resolved	$0.002	Each successfully resolved company row
filing	$0.002	Each filing row
financial-fact	$0.0005	Each XBRL fact row
text-excerpt	$0.003	Each primary-document text excerpt row

Primary event for Store display: company-resolved.

Memory / runtime

Default 256 MB, timeout 300s. HTTP-only (no browser).

MCP / Apify tools

Call this public Actor from agents via Apify MCP:

MCP server URL (scoped tools):

https://mcp.apify.com/?tools=fetch-actor-details,fairquery/edgar-company-facts-filings

1. Connect your MCP client to that URL (or https://mcp.apify.com) with an Apify token.
2. Use Actor id fairquery/edgar-company-facts-filings (lowercase username).
3. Pass JSON input as below; read the default dataset items and RUN_SUMMARY.

Example conceptual MCP / Actor call:

{
  "actor": "fairquery/edgar-company-facts-filings",
  "input": {
    "companies": ["AAPL"],
    "mode": "facts",
    "metrics": ["Assets", "NetIncomeLoss"]
  }
}

Local development

npm install
# put input in storage/key_value_stores/default/INPUT.json
npx apify run
npm test

Optional local PPE dry-run: ACTOR_TEST_PAY_PER_EVENT=1 npx apify run.

Kits & templates

Optional guides from Fairquery for wiring this Actor into agents and automations (the Actor works fine without them):

• AI Agent Data Toolkit Quick-Start (PDF): MCP setup for Claude Desktop and Cursor, API calls, recipes, cost estimates.
• n8n + Zapier Workflow Template Pack: 10 importable n8n workflows for the four Fairquery Actors, plus Zapier setup guides.
• Agent Prompt Pack for Research & Lead Ops: 69 prompts for company research, lead cleaning, SEO and security reviews.

Disclaimer

Data originates from the U.S. Securities and Exchange Commission EDGAR system. Fairquery and this Actor provide no warranty of completeness or fitness for trading decisions. This is not investment advice.