# Fec Spending Scraper (`quarterly_jingo/fec-spending-scraper`) Actor - **URL**: https://apify.com/quarterly\_jingo/fec-spending-scraper.md - **Developed by:** [Petey Boy](https://apify.com/quarterly_jingo) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## FEC Campaign Spending Scraper - Disbursements, Super PAC & Political Spending Tracker ### Overview Track where campaign money goes. Scrape FEC disbursements (Schedule B), independent expenditures (Schedule E), and committee totals. Monitor Super PAC spending, vendor payments, ad buys, and political consulting fees. ### Features - Search by keywords to find specific results - Filter results by category or type - Export data in JSON, CSV, or Excel formats - Control output volume with configurable result limits ### Use Cases - **Track** - Track federal government data releases and updates - **Build** - Build datasets for policy research and analysis - **Monitor** - Monitor regulatory changes and compliance requirements - **Aggregate** - Aggregate public government data for transparency projects ### Input Parameters | Parameter | Type | Description | Default | |-----------|------|-------------|---------| | `apiKey` | string **(required)** | Your API key from api.data.gov (free signup). DEMO_KEY works for testing but ... | `DEMO_KEY` | | `mode` | string **(required)** | What type of spending data to retrieve | `disbursements` | | `committeeId` | string | FEC Committee ID to filter by (e.g., C00703975 for ActBlue). Required for com... | | | `candidateId` | string | FEC Candidate ID for independent expenditure tracking (e.g., P80001571 for Bi... | | | `recipientName` | string | Filter disbursements by who received payment (partial match supported) | | | `twoYearPeriod` | integer | Two-year election cycle (e.g., 2024 for 2023-2024 cycle) | | | `minAmount` | integer | Minimum dollar amount to include | | | `maxAmount` | integer | Maximum dollar amount to include | | | `maxResults` | integer | Maximum number of records to return | `100` | | `supportOppose` | string | For independent expenditures: filter by whether spending supports or opposes ... | `all` | ### Output Example Each result contains structured data like this: ```json { "committee_id": "ABC-12345", "committee_name": "FEC Campaign Spending Sample Item", "recipient_name": "FEC Campaign Spending Sample Item", "candidate_name": "FEC Campaign Spending Sample Item", "disbursement_amount": 50000, "expenditure_amount": 50000, "disbursement_date": "2025-01-15", "expenditure_date": "2025-01-15", "disbursement_description": "Sample disbursement_description", "expenditure_description": "Sample expenditure_description", "support_oppose": "Sample support_oppose" } ```` ### Pricing This actor uses pay-per-result pricing: - **$0.001** per result - **$1.00** per 1,000 results No monthly fees. You only pay for what you scrape. [Apify Free plan](https://apify.com/pricing) includes $5/month in platform credits. ### How to Run #### Apify Console 1. Go to the [FEC Campaign Spending Scraper - Disbursements, Super PAC & Political Spending Tracker](https://apify.com/fortuitous_pirate/fec-spending-scraper) actor page 2. Configure your input parameters 3. Click **Start** and wait for the results 4. Download data in JSON, CSV, or Excel format #### API ```bash curl -X POST "https://api.apify.com/v2/acts/fortuitous_pirate~fec-spending-scraper/runs?token=YOUR_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{"maxResults": 10}' ``` #### Python SDK ```python from apify_client import ApifyClient client = ApifyClient("YOUR_API_TOKEN") run = client.actor("fortuitous_pirate/fec-spending-scraper").call( run_input={"maxResults": 10} ) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) ``` ### Integration Connect FEC Campaign Spending Scraper - Disbursements, Super PAC & Political Spending Tracker with your existing tools and workflows: - **API access** - Programmatic access via [Apify API](https://docs.apify.com/api/v2) - **Webhooks** - Get notified when scraping completes - **Scheduling** - Set up recurring runs on any schedule - **Zapier / Make** - Connect with 5,000+ apps via [Apify integrations](https://apify.com/integrations) - **Python / Node.js SDKs** - Native client libraries for easy integration # Actor input Schema ## `apiKey` (type: `string`): Your API key from api.data.gov (free signup). DEMO\_KEY works for testing but has stricter rate limits. ## `mode` (type: `string`): What type of spending data to retrieve ## `committeeId` (type: `string`): FEC Committee ID to filter by (e.g., C00703975 for ActBlue). Required for committee-totals mode. ## `candidateId` (type: `string`): FEC Candidate ID for independent expenditure tracking (e.g., P80001571 for Biden) ## `recipientName` (type: `string`): Filter disbursements by who received payment (partial match supported) ## `twoYearPeriod` (type: `integer`): Two-year election cycle (e.g., 2024 for 2023-2024 cycle) ## `minAmount` (type: `integer`): Minimum dollar amount to include ## `maxAmount` (type: `integer`): Maximum dollar amount to include ## `maxResults` (type: `integer`): Maximum number of records to return ## `supportOppose` (type: `string`): For independent expenditures: filter by whether spending supports or opposes a candidate ## Actor input object example ```json { "apiKey": "DEMO_KEY", "mode": "disbursements", "maxResults": 100, "supportOppose": "all" } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("quarterly_jingo/fec-spending-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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("quarterly_jingo/fec-spending-scraper").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 '{}' | apify call quarterly_jingo/fec-spending-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=quarterly_jingo/fec-spending-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fec Spending Scraper", "description": "", "version": "1.0", "x-build-id": "q1eFRHH6RPVB6SON9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/quarterly_jingo~fec-spending-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-quarterly_jingo-fec-spending-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/quarterly_jingo~fec-spending-scraper/runs": { "post": { "operationId": "runs-sync-quarterly_jingo-fec-spending-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/quarterly_jingo~fec-spending-scraper/run-sync": { "post": { "operationId": "run-sync-quarterly_jingo-fec-spending-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "apiKey", "mode" ], "properties": { "apiKey": { "title": "FEC API Key", "type": "string", "description": "Your API key from api.data.gov (free signup). DEMO_KEY works for testing but has stricter rate limits.", "default": "DEMO_KEY" }, "mode": { "title": "Data Mode", "enum": [ "disbursements", "independent-expenditures", "committee-totals" ], "type": "string", "description": "What type of spending data to retrieve", "default": "disbursements" }, "committeeId": { "title": "Committee ID", "type": "string", "description": "FEC Committee ID to filter by (e.g., C00703975 for ActBlue). Required for committee-totals mode." }, "candidateId": { "title": "Candidate ID", "type": "string", "description": "FEC Candidate ID for independent expenditure tracking (e.g., P80001571 for Biden)" }, "recipientName": { "title": "Recipient Name", "type": "string", "description": "Filter disbursements by who received payment (partial match supported)" }, "twoYearPeriod": { "title": "Election Cycle", "minimum": 1980, "maximum": 2030, "type": "integer", "description": "Two-year election cycle (e.g., 2024 for 2023-2024 cycle)" }, "minAmount": { "title": "Minimum Amount", "minimum": 0, "type": "integer", "description": "Minimum dollar amount to include" }, "maxAmount": { "title": "Maximum Amount", "type": "integer", "description": "Maximum dollar amount to include" }, "maxResults": { "title": "Maximum Results", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of records to return", "default": 100 }, "supportOppose": { "title": "Support/Oppose Filter", "enum": [ "all", "support", "oppose" ], "type": "string", "description": "For independent expenditures: filter by whether spending supports or opposes a candidate", "default": "all" } } }, "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 } } } } } } } } } } ```