# SEC EDGAR Filings Search, Monitor & AI Extractor (`schmarta/sec-edgar-filings-search-monitor`) Actor Search, monitor and extract SEC EDGAR filings (8-K, 10-K, 10-Q, S-1, 13F…). Full-text search, delta-only monitoring, LLM-ready markdown, XBRL financials. Works as an MCP server for AI agents. - **URL**: https://apify.com/schmarta/sec-edgar-filings-search-monitor.md - **Developed by:** [Lucas Marta](https://apify.com/schmarta) (community) - **Categories:** AI, Automation - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 filing results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use 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 Filings Search, Monitor & AI Extractor Search SEC EDGAR filings by full text, monitor companies for new filings, and extract any filing as clean, **LLM-ready markdown** — 8-K, 10-K, 10-Q, S-1, 13F, Form 4 and every other form the SEC publishes. Built directly on the SEC's official EDGAR APIs, so results are complete, first-party, and legal to use. Unlike raw EDGAR, this Actor gives you **full-text search + filing history + document extraction + monitoring in one tool**, returns structured JSON you can pipe anywhere, and doubles as an **MCP server** so AI agents (Claude, Cursor, custom agents) can query SEC filings directly. ### How to search SEC EDGAR filings 1. Choose a mode: - **Search** — full-text search across all filing documents (e.g. `"cybersecurity incident"`, `"material weakness"`, `"going concern"`). - **Company** — pull the filing history of specific companies by ticker (`AAPL`) or CIK (`320193`). - **Monitor** — return **only filings you haven't seen in previous runs**. Put it on an Apify Schedule and get a clean feed of new filings. 2. Optionally filter by form type (8-K, 10-K, 10-Q, …) and filing date. 3. Optionally turn on **LLM-ready markdown extraction** to get the full document text, tables preserved, ready for RAG pipelines, summarization, or AI analysis. ### How to monitor SEC filings automatically Set mode to `monitor`, add your companies and/or query, and create a Schedule (e.g. every hour). The Actor remembers which filings it already delivered — every run returns only the delta. Typical uses: - Alert when a portfolio company files an 8-K with Item 1.05 (material cybersecurity incident) - Track competitor S-1s, 13D/G activist stakes, or insider Form 4s - Feed new 10-K risk factors straight into your AI pipeline ### Output Each filing is one dataset item: | Field | Description | |---|---| | `accessionNumber` | SEC accession number (unique filing id) | | `cik`, `ticker`, `companyName` | Filer identity | | `formType` | 8-K, 10-K, 10-Q, S-1, … | | `filedAt`, `periodOfReport` | Dates | | `filingUrl`, `documentUrls` | Links to the filing index and documents | | `eightKItems` | 8-K item codes (e.g. `1.05`, `5.02`) | | `fullTextMarkdown` | Full document as clean markdown (when enabled) | | `isNew` | Whether this filing is new since the last run | ### Use as an MCP server (AI agents) This Actor runs in Standby mode as a streamable-HTTP MCP server. Point your agent at it via [mcp.apify.com](https://mcp.apify.com) and it gets three tools: - `search_sec_filings` — full-text search with form/date filters - `get_filing_as_markdown` — any filing as clean markdown - `get_company_financial_facts` — standardized XBRL financials (revenue, income, assets…) by ticker or CIK ### Pricing Pay per event — you only pay for what you extract: | Event | Price | |---|---| | Actor start | $0.005 | | Filing result (metadata) | $0.001 | | Full-text markdown document | $0.01 | A scheduled monitor over 20 tickers typically costs **$0.02–0.10 per run**. Compare with commercial EDGAR APIs starting at $99+/month. ### FAQ **Is scraping SEC EDGAR legal?** This Actor uses the SEC's official public APIs (data.sec.gov, efts.sec.gov). The data is US-government public domain. The Actor respects the SEC fair-access rate limit (max 5 requests/second with an identifying User-Agent). **How fresh is the data?** EDGAR full-text search indexes filings within minutes of dissemination; `monitor` mode picks them up on your schedule. **Which forms are supported?** All of them. The form-type filter has quick picks for the common ones (8-K, 10-K, 10-Q, S-1, 13F-HR, SC 13D/G, Form 4, DEF 14A, 6-K, 20-F), and you can pass any other form type via the API. **Can I get financial statements?** Yes — the `get_company_financial_facts` MCP tool (or the XBRL companyfacts endpoint through the Actor) returns standardized financials for any reporting company. # Actor input Schema ## `mode` (type: `string`): **search**: one-off full-text search. **company**: filing history for specific companies. **monitor**: only new filings since the last run — ideal on a Schedule. ## `query` (type: `string`): Phrase to search across all filing documents, e.g. `cybersecurity incident`, `material weakness`, `going concern`. ## `companies` (type: `array`): Tickers (`AAPL`) or CIK numbers (`320193`). Leave empty when only using full-text search. ## `formTypes` (type: `array`): Limit to specific SEC forms. Empty = all forms. ## `dateFrom` (type: `string`): YYYY-MM-DD. Empty = no lower bound. ## `dateTo` (type: `string`): YYYY-MM-DD. Empty = today. ## `includeFullText` (type: `boolean`): Fetch each filing document and convert it to clean markdown (tables preserved) — ready for RAG pipelines and AI agents. Charged per document. ## `maxResults` (type: `integer`): Maximum filings per run. ## `deltaOnly` (type: `boolean`): Persists seen filings per query in a key-value store. Defaults to ON in monitor mode, OFF otherwise. ## `contactEmail` (type: `string`): SEC's fair-access policy asks for an identifying User-Agent contact. Optional but recommended for heavy use. ## Actor input object example ```json { "mode": "search", "query": "cybersecurity incident", "companies": [], "formTypes": [ "8-K" ], "includeFullText": false, "maxResults": 100 } ```` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "query": "cybersecurity incident", "formTypes": [ "8-K" ] }; // Run the Actor and wait for it to finish const run = await client.actor("schmarta/sec-edgar-filings-search-monitor").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "query": "cybersecurity incident", "formTypes": ["8-K"], } # Run the Actor and wait for it to finish run = client.actor("schmarta/sec-edgar-filings-search-monitor").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 '{ "query": "cybersecurity incident", "formTypes": [ "8-K" ] }' | apify call schmarta/sec-edgar-filings-search-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=schmarta/sec-edgar-filings-search-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SEC EDGAR Filings Search, Monitor & AI Extractor", "description": "Search, monitor and extract SEC EDGAR filings (8-K, 10-K, 10-Q, S-1, 13F…). Full-text search, delta-only monitoring, LLM-ready markdown, XBRL financials. Works as an MCP server for AI agents.", "version": "0.1", "x-build-id": "PlRoxvQz045U04xJ0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/schmarta~sec-edgar-filings-search-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-schmarta-sec-edgar-filings-search-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/schmarta~sec-edgar-filings-search-monitor/runs": { "post": { "operationId": "runs-sync-schmarta-sec-edgar-filings-search-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/schmarta~sec-edgar-filings-search-monitor/run-sync": { "post": { "operationId": "run-sync-schmarta-sec-edgar-filings-search-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "mode": { "title": "🔍 Mode", "enum": [ "search", "company", "monitor" ], "type": "string", "description": "**search**: one-off full-text search. **company**: filing history for specific companies. **monitor**: only new filings since the last run — ideal on a Schedule.", "default": "search" }, "query": { "title": "🔎 Full-text query", "type": "string", "description": "Phrase to search across all filing documents, e.g. `cybersecurity incident`, `material weakness`, `going concern`." }, "companies": { "title": "🏢 Companies (tickers or CIKs)", "type": "array", "description": "Tickers (`AAPL`) or CIK numbers (`320193`). Leave empty when only using full-text search.", "default": [], "items": { "type": "string" } }, "formTypes": { "title": "📄 Form types", "type": "array", "description": "Limit to specific SEC forms. Empty = all forms.", "items": { "type": "string", "enum": [ "8-K", "10-K", "10-Q", "S-1", "424B4", "13F-HR", "SC 13D", "SC 13G", "4", "DEF 14A", "6-K", "20-F" ] } }, "dateFrom": { "title": "📅 Filed after", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "type": "string", "description": "YYYY-MM-DD. Empty = no lower bound." }, "dateTo": { "title": "📅 Filed before", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "type": "string", "description": "YYYY-MM-DD. Empty = today." }, "includeFullText": { "title": "🤖 Extract full text as LLM-ready markdown", "type": "boolean", "description": "Fetch each filing document and convert it to clean markdown (tables preserved) — ready for RAG pipelines and AI agents. Charged per document.", "default": false }, "maxResults": { "title": "Max results", "maximum": 1000, "type": "integer", "description": "Maximum filings per run.", "default": 100 }, "deltaOnly": { "title": "Delta only (skip filings already seen in previous runs)", "type": "boolean", "description": "Persists seen filings per query in a key-value store. Defaults to ON in monitor mode, OFF otherwise." }, "contactEmail": { "title": "Contact email for SEC User-Agent", "type": "string", "description": "SEC's fair-access policy asks for an identifying User-Agent contact. Optional but recommended for heavy use." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```