# Fec Filing Parser (`logical_vivacity/fec-filing-parser`) Actor Turn raw U.S. Federal Election Commission electronic filings into clean, structured JSON you can drop straight into a database, spreadsheet, or analytics pipeline. - **URL**: https://apify.com/logical\_vivacity/fec-filing-parser.md - **Developed by:** [Logical Vivacity](https://apify.com/logical_vivacity) (community) - **Categories:** Developer tools, Automation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 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/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 ## FEC Campaign Finance Filing Parser Turn raw U.S. Federal Election Commission electronic filings into clean, structured JSON you can drop straight into a database, spreadsheet, or analytics pipeline. Give the actor one or more filing IDs and it returns a header / summary record plus every itemized contribution, disbursement, or loan row contained in the filing. ### What it does - Fetches the raw `.fec` document for each filing ID you supply from the FEC's public electronic-filings index. - Parses the filing's fixed-format text into typed Python objects. - Splits the result into: - One **filing header** record (filer name, committee ID, period covered, totals, etc.). - One **itemized row** record for each Schedule A / B / C / D / E line item, tagged with its schedule bucket. - Pushes every record to the actor's default dataset. ### Inputs | Field | Type | Default | Description | | --- | --- | --- | --- | | `filingIds` | array of string / integer | _required_ | FEC electronic filing IDs to download and parse, e.g. `["1666999"]`. | | `includeItemizedRows` | boolean | `true` | If `false`, only the filing header / summary is returned. | | `maxRowsPerFiling` | integer | `10000` | Cap on itemized rows returned per filing. Large filings can contain 100,000+ rows. | #### Example input ```json { "filingIds": ["1666999"], "includeItemizedRows": true, "maxRowsPerFiling": 10000 } ```` ### Output Every record is pushed to the default dataset and tagged with a `record_type` of either `filing_header` or `itemized_row`. #### Sample header record ```json { "record_type": "filing_header", "filing_id": "1666999", "source_url": "https://docquery.fec.gov/dcdev/posted/1666999.fec", "data": { "header": { "record_type": "HDR", "ef_type": "FEC", "fec_version": "8.4", "soft_name": "...", "report_id": "..." }, "filer_committee_id_number": "C00000000", "committee_name": "EXAMPLE FOR CONGRESS", "form_type": "F3", "coverage_from_date": "2024-01-01", "coverage_through_date": "2024-03-31", "total_receipts_period": 123456.78 } } ``` #### Sample itemized row record ```json { "record_type": "itemized_row", "filing_id": "1666999", "schedule": "Schedule A", "row_index": 0, "data": { "form_type": "SA11AI", "filer_committee_id_number": "C00000000", "contributor_last_name": "Smith", "contributor_first_name": "Jane", "contributor_city": "Austin", "contributor_state": "TX", "contribution_date": "2024-02-14", "contribution_amount": 250.0, "contribution_aggregate": 750.0, "contributor_employer": "Acme Corp", "contributor_occupation": "Engineer" } } ``` The exact fields available depend on the form type (F3, F3X, F24, etc.) and the schedule (A = receipts, B = disbursements, C = loans, D = debts, E = independent expenditures). ### Limitations - The FEC's public document host can be slow or temporarily unavailable; failed fetches are logged and skipped without aborting the run. - Very large filings (50,000+ itemizations) are split into thousands of dataset records — raise `maxRowsPerFiling` if you need the full extract. - Only electronic filings are supported. Paper-filed reports do not have a raw `.fec` document and cannot be parsed. - Field names follow the FEC's published form schemas. See [fec.gov filing types](https://www.fec.gov/help-candidates-and-committees/filing-reports/) for the canonical column dictionary. # Actor input Schema ## `filingIds` (type: `array`): List of FEC electronic filing IDs to fetch and parse (e.g. \["1666999"]). Numeric strings or integers both accepted. ## `includeItemizedRows` (type: `boolean`): If true, output each itemized contribution / disbursement / loan row in addition to the filing header. If false, only the filing header / summary is returned. ## `maxRowsPerFiling` (type: `integer`): Cap on itemized rows returned per filing. Large filings can contain 100,000+ rows; raise this for full extracts. ## Actor input object example ```json { "filingIds": [ "1666999" ], "includeItemizedRows": true, "maxRowsPerFiling": 10000 } ``` # 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 = { "filingIds": [ "1666999" ] }; // Run the Actor and wait for it to finish const run = await client.actor("logical_vivacity/fec-filing-parser").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 = { "filingIds": ["1666999"] } # Run the Actor and wait for it to finish run = client.actor("logical_vivacity/fec-filing-parser").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 '{ "filingIds": [ "1666999" ] }' | apify call logical_vivacity/fec-filing-parser --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=logical_vivacity/fec-filing-parser", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fec Filing Parser", "description": "Turn raw U.S. Federal Election Commission electronic filings into clean, structured JSON you can drop straight into a database, spreadsheet, or analytics pipeline.", "version": "0.1", "x-build-id": "Y44g5Prs16P2PBhQN" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/logical_vivacity~fec-filing-parser/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-logical_vivacity-fec-filing-parser", "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/logical_vivacity~fec-filing-parser/runs": { "post": { "operationId": "runs-sync-logical_vivacity-fec-filing-parser", "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/logical_vivacity~fec-filing-parser/run-sync": { "post": { "operationId": "run-sync-logical_vivacity-fec-filing-parser", "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": [ "filingIds" ], "properties": { "filingIds": { "title": "Filing IDs", "type": "array", "description": "List of FEC electronic filing IDs to fetch and parse (e.g. [\"1666999\"]). Numeric strings or integers both accepted." }, "includeItemizedRows": { "title": "Include itemized rows", "type": "boolean", "description": "If true, output each itemized contribution / disbursement / loan row in addition to the filing header. If false, only the filing header / summary is returned.", "default": true }, "maxRowsPerFiling": { "title": "Max rows per filing", "minimum": 0, "type": "integer", "description": "Cap on itemized rows returned per filing. Large filings can contain 100,000+ rows; raise this for full extracts.", "default": 10000 } } }, "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 } } } } } } } } } } ```