# House Financial Disclosures Scraper (`automation-lab/house-financial-disclosures-scraper`) Actor ๐Ÿ›๏ธ Extract U.S. House Clerk financial disclosure and PTR filing metadata with direct public PDF links for members and candidates. - **URL**: https://apify.com/automation-lab/house-financial-disclosures-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** Other - **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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## House Financial Disclosures Scraper Extract searchable U.S. House Clerk financial disclosure filings, periodic transaction reports, and candidate disclosure metadata from the official public portal. ### What does House Financial Disclosures Scraper do? House Financial Disclosures Scraper searches the U.S. House Clerk financial disclosure database and returns structured dataset rows for House members and candidates. It captures the same records visible in the public search interface, including member/candidate names, office codes, filing years, filing types, and direct PDF document links. Use it to monitor financial disclosure reports, periodic transaction reports (PTRs), and candidate filings without manually clicking through the House Clerk website. ### Who is it for? This actor is designed for teams that need repeatable government disclosure monitoring. - ๐Ÿ“ฐ Journalists tracking congressional stock trading and annual disclosure reports - ๐Ÿ“ˆ Investors and political-risk analysts monitoring market-moving disclosures - ๐Ÿงพ Compliance teams reviewing public official filing records - ๐Ÿ›๏ธ Transparency NGOs building searchable disclosure archives - ๐Ÿ”Ž Researchers matching House disclosure PDFs to broader public datasets - โš–๏ธ Legal and ethics teams checking filing availability by year and office ### Why use this actor? The House Clerk portal is public, but manual workflows are slow. This actor gives you API-ready rows that can be exported to JSON, CSV, Excel, Google Sheets, BigQuery, or any Apify integration. It is useful for scheduled monitoring because disclosure data changes over time as new annual filings, PTRs, amendments, and candidate reports are posted. ### What data can I extract? The actor extracts one dataset item per filing row returned by the House Clerk search result table. | Field | Description | |---|---| | `searchType` | Whether the row came from member or candidate search | | `name` | Name exactly as listed by the House Clerk | | `normalizedName` | Cleaned version of the name for matching | | `office` | Office code such as `CA11` | | `state` | Parsed state or territory code | | `district` | Parsed House district | | `filingYear` | Member filing year when applicable | | `electionYear` | Candidate election year when applicable | | `filing` | Filing label such as `FD Original` or `PTR Original` | | `filingCategory` | Normalized category such as Financial Disclosure or Periodic Transaction Report | | `documentUrl` | Direct PDF URL for the filing | | `documentPath` | Original relative PDF path | | `sourceSearchUrl` | Source search page | | `scrapedAt` | Extraction timestamp | ### How much does it cost to scrape House financial disclosures? This actor uses pay-per-event pricing. You pay a small start fee per run and a per-result fee for each filing row saved to the dataset. The default input is intentionally small so first runs are cheap. Increase `maxItems`, years, and states when you are ready for larger exports. ### How to use House Financial Disclosures Scraper 1. Open the actor on Apify. 2. Choose `members`, `candidates`, or `both`. 3. Enter filing years or election years. 4. Optionally filter by state, district, or last name. 5. Set `maxItems`. 6. Run the actor. 7. Download results from the Dataset tab. ### Input options #### Search type Choose which House Clerk database to query. - `members` searches member financial disclosure filings. - `candidates` searches candidate filings. - `both` searches both endpoints and combines rows in one dataset. #### Filing years `filingYears` controls member searches. Example: ```json [2024, 2025] ```` #### Election years `electionYears` controls candidate searches. Example: ```json [2024] ``` #### States Use two-letter state or territory codes. Example: ```json ["CA", "NY", "TX"] ``` Leave the array empty to search all states for the selected year. #### Districts Use district numbers such as `11`, `05`, or `AL`. Leave empty to search all districts. #### Last names Use surnames when you need a specific official or candidate. Example: ```json ["Pelosi", "Smith"] ``` Leave empty to return all names matching the year/state filters. #### Maximum filings `maxItems` stops the run after the requested number of dataset rows. Use a low value for tests and scheduled smoke checks. ### Example input ```json { "searchType": "members", "filingYears": [2024], "states": ["CA"], "maxItems": 100 } ``` ### Example output ```json { "searchType": "member", "name": "Pelosi, Hon.. Nancy", "normalizedName": "Pelosi, Hon. Nancy", "lastNameSearched": null, "office": "CA11", "state": "CA", "district": "11", "filingYear": 2024, "electionYear": null, "filing": "PTR Original", "filingCategory": "Periodic Transaction Report", "documentUrl": "https://disclosures-clerk.house.gov/public_disc/ptr-pdfs/2024/20024542.pdf", "documentPath": "public_disc/ptr-pdfs/2024/20024542.pdf", "sourceSearchUrl": "https://disclosures-clerk.house.gov/FinancialDisclosure", "scrapedAt": "2026-06-28T00:00:00.000Z" } ``` ### Tips for best results - Start with one year and one state. - Use `lastNames` for targeted monitoring. - Use `both` only when you need member and candidate filings together. - Increase `maxItems` for statewide or national exports. - Schedule recurring runs to detect newly posted PTRs. ### Monitoring workflows You can run this actor daily or weekly and compare new `documentUrl` values against prior datasets. Common workflows include: - new PTR alerts by member - annual FD report availability checks - candidate filing collection by election year - state-level disclosure monitoring - PDF archive refreshes ### Integrations Apify datasets connect to many downstream tools. Useful integrations include: - Google Sheets for analyst review - Slack or email alerts for new filings - BigQuery or Snowflake for historical analysis - Webhooks for compliance workflows - Make or Zapier for no-code monitoring pipelines ### API usage with Node.js ```js import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); const run = await client.actor('automation-lab/house-financial-disclosures-scraper').call({ searchType: 'members', filingYears: [2024], states: ['CA'], maxItems: 100, }); console.log(run.defaultDatasetId); ``` ### API usage with Python ```python from apify_client import ApifyClient client = ApifyClient("") run = client.actor("automation-lab/house-financial-disclosures-scraper").call(run_input={ "searchType": "members", "filingYears": [2024], "states": ["CA"], "maxItems": 100, }) print(run["defaultDatasetId"]) ``` ### API usage with cURL ```bash curl "https://api.apify.com/v2/acts/automation-lab~house-financial-disclosures-scraper/runs?token=$APIFY_TOKEN" \ -H 'Content-Type: application/json' \ -d '{"searchType":"members","filingYears":[2024],"states":["CA"],"maxItems":100}' ``` ### MCP access Use this actor from MCP-compatible clients through Apify MCP Server. MCP URL: ```text https://mcp.apify.com/?tools=automation-lab/house-financial-disclosures-scraper ``` Claude Code setup command: ```bash claude mcp add apify-house-disclosures https://mcp.apify.com/?tools=automation-lab/house-financial-disclosures-scraper ``` Claude Desktop JSON configuration: ```json { "mcpServers": { "apify-house-disclosures": { "url": "https://mcp.apify.com/?tools=automation-lab/house-financial-disclosures-scraper" } } } ``` Example prompt showing MCP usage: Example Claude Desktop prompt: ```text Use the Apify MCP House Financial Disclosures Scraper to find 2024 CA member PTR filings and summarize the PDF links. ``` Example Claude Code prompt: ```text Using the MCP tool automation-lab/house-financial-disclosures-scraper, run member filings in CA for 2024 with maxItems 100, then save the dataset URLs. ``` ### Data source The source is the official U.S. House Clerk Financial Disclosure Reports portal. The actor does not bypass authentication, scrape private data, or access non-public endpoints. It returns public filing metadata and direct public PDF links exposed by the House Clerk search pages. ### Legality and responsible use The House Clerk page includes statutory restrictions on how financial disclosure information may be used. Users are responsible for complying with applicable law, including restrictions on unlawful use, credit rating use, solicitation, and other prohibited purposes. This actor is intended for lawful research, journalism, compliance, transparency, and public-interest monitoring. ### Limitations - The actor extracts filing metadata and PDF links, not full PDF text. - The actor covers House Clerk financial disclosure pages, not Senate EFD. - The actor depends on the public House Clerk search form structure. - Search result availability is controlled by the official portal. ### FAQ #### Why did my run return fewer rows than expected? Check `maxItems`, state, district, year, and last-name filters. A narrow last-name search may legitimately return few or no rows. #### Why are candidate fields different from member fields? Candidate searches use election years, while member searches use filing years. The output keeps both `filingYear` and `electionYear` so mixed datasets stay clear. #### Can I download the PDFs? The dataset includes direct public PDF URLs. You can pass `documentUrl` values to a downloader or document-processing pipeline. ### Related scrapers Explore related Automation Lab actors: - https://apify.com/automation-lab/sec-edgar-scraper - https://apify.com/automation-lab/fec-campaign-finance-scraper - https://apify.com/automation-lab/government-contracts-scraper - https://apify.com/automation-lab/court-listener-scraper ### Support If you need additional fields, PDF text extraction, or Senate EFD coverage, open an Apify issue with your target workflow and example inputs. # Actor input Schema ## `searchType` (type: `string`): Choose House member filings, candidate filings, or both databases. ## `filingYears` (type: `array`): Years to search for House member filings. Used for Members and Both modes. ## `electionYears` (type: `array`): Election years to search for candidate filings. Used for Candidates and Both modes. ## `states` (type: `array`): Two-letter postal abbreviations. Leave empty to search all states for the selected year. ## `districts` (type: `array`): District numbers such as 11, 05, or AL. Leave empty for all districts in the selected state. ## `lastNames` (type: `array`): Optional last names to search, for example Pelosi or Smith. Leave empty for all names matching the year/state filters. ## `maxItems` (type: `integer`): Maximum disclosure records to save to the dataset. ## Actor input object example ```json { "searchType": "members", "filingYears": [ 2024 ], "electionYears": [ 2024 ], "states": [ "CA" ], "districts": [], "lastNames": [], "maxItems": 20 } ``` # Actor output Schema ## `overview` (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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "searchType": "members", "filingYears": [ 2024 ], "electionYears": [ 2024 ], "states": [ "CA" ], "districts": [], "lastNames": [], "maxItems": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/house-financial-disclosures-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "searchType": "members", "filingYears": [2024], "electionYears": [2024], "states": ["CA"], "districts": [], "lastNames": [], "maxItems": 20, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/house-financial-disclosures-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "searchType": "members", "filingYears": [ 2024 ], "electionYears": [ 2024 ], "states": [ "CA" ], "districts": [], "lastNames": [], "maxItems": 20 }' | apify call automation-lab/house-financial-disclosures-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/house-financial-disclosures-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "House Financial Disclosures Scraper", "description": "๐Ÿ›๏ธ Extract U.S. House Clerk financial disclosure and PTR filing metadata with direct public PDF links for members and candidates.", "version": "0.1", "x-build-id": "mA1cPLPPvG9csPdAX" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~house-financial-disclosures-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-house-financial-disclosures-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~house-financial-disclosures-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-house-financial-disclosures-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~house-financial-disclosures-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-house-financial-disclosures-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchType": { "title": "Search type", "enum": [ "members", "candidates", "both" ], "type": "string", "description": "Choose House member filings, candidate filings, or both databases.", "default": "members" }, "filingYears": { "title": "Member filing years", "type": "array", "description": "Years to search for House member filings. Used for Members and Both modes.", "items": { "type": "integer", "minimum": 2008, "maximum": 2026 } }, "electionYears": { "title": "Candidate election years", "type": "array", "description": "Election years to search for candidate filings. Used for Candidates and Both modes.", "items": { "type": "integer", "minimum": 2011, "maximum": 2027 } }, "states": { "title": "States or territories", "type": "array", "description": "Two-letter postal abbreviations. Leave empty to search all states for the selected year.", "items": { "type": "string" } }, "districts": { "title": "Districts", "type": "array", "description": "District numbers such as 11, 05, or AL. Leave empty for all districts in the selected state.", "items": { "type": "string" } }, "lastNames": { "title": "Last names", "type": "array", "description": "Optional last names to search, for example Pelosi or Smith. Leave empty for all names matching the year/state filters.", "items": { "type": "string" } }, "maxItems": { "title": "Maximum filings", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum disclosure records to save to the dataset.", "default": 20 } } }, "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 } } } } } } } } } } ```