# Dataset Freshness SLA Monitoring (`zentrafoundry/dataset-freshness-sla-monitoring`) Actor Monitor official tender portals, procurement notices, buyer pages, public documents, and configured source URLs and return tender notice records with buyer, deadline, category, source URL, and action-ready context. - **URL**: https://apify.com/zentrafoundry/dataset-freshness-sla-monitoring.md - **Developed by:** [Zentra](https://apify.com/zentrafoundry) (community) - **Categories:** E-commerce, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $43.00 / 1,000 apify actor run or buyer defined paid events 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 ## Dataset Freshness SLA Monitoring Monitor official tender portals, procurement notices, buyer pages, public documents, and configured source URLs and return tender notice records with buyer, deadline, category, source URL, and action-ready context. ### What this Actor does ### What it does - Processes configured public sources or user-provided records for focused dataset freshness sla monitoring monitoring. - Emits structured rows with source references, stable identifiers, confidence, warnings, and run summary fields. - Supports sample-mode runs so Apify Store QA and first-time users can inspect output without depending on live third-party availability. ### What it does not do - Does not scrape private, login-only, paywalled, or access-restricted data unless the user provides approved credentials for a source they control. - Does not guarantee every field is available from every source; missing or blocked fields are returned as warnings or nulls. - Does not make legal, financial, compliance, procurement, medical, safety, or regulatory decisions. ### Who this is for Vendors, suppliers, contractors, consultants, sales teams, and bid teams use this actor when they need focused dataset freshness sla monitoring output instead of a broad generic scraper or manual checking. ### Buyer outcomes - Find relevant dataset freshness sla monitoring opportunities faster with normalized source evidence and match context. - Prioritize review using buyer names, deadlines, categories, source URLs, scores, and confidence. - Route new or changed opportunities into recurring CRM, spreadsheet, bid, or alert workflows. ### Data sources ### Sources monitored official tender portals, procurement notices, buyer pages, public documents, and configured source URLs ### Input - `sourceMode`: use `sample` for a smoke run or configured public tender source modes. - `startUrls`: official tender notices, buyer pages, procurement documents, or source URLs. - `sourceIds`: approved public procurement source identifiers. - `maxItems`: bounded number of tender notices to return. - `sinceLastRun`: emit only new or changed notices when scheduled. - `watchlistTerms`: buyer, CPV/category, region, deadline, value, or requirement terms. - `webhookUrl`: optional destination for bid, CRM, spreadsheet, or alert workflows. - `outputMode`: use sample records for Store validation or production output for normal runs. ### How it transforms the input - Input: official tender notice, procurement portal result, buyer page, or public tender document. - Transformation: normalize the notice, preserve source evidence, and apply buyer/category/deadline filters. - Output: tender notice with buyer, deadline, category, value when available, source URL, match reason, and confidence. ### Output The actor returns structured tender notice records with buyer, deadline, region, category, CPV/category hints, value when available, source URL, match reason, and confidence. Family-specific fields to expect: - `buyerName`: Public buyer or awarding authority. - `deadline`: Submission or response deadline when available. - `category`: Tender category or CPV/category hint. - `sourceUrl`: Official notice or document URL. - `matchReason`: Why the notice matched the actor focus. - `recordId`: Stable record ID for exports, dedupe, and downstream joins. - `title`: Human-readable record title for review and export. - `sourceName`: Source identifier used to trace where the record came from. - `sourceUrl`: Direct source URL for review and audit. - `dedupeKey`: Stable key used for delta mode and duplicate suppression. - `retrievedAt`: Timestamp showing when the actor retrieved or generated this record. - `score`: Normalized field for filtering, routing, or downstream review. - `scoreReasons`: Buyer-readable explanation for the score or match. - `confidence`: Normalized field for filtering, routing, or downstream review. - `errors`: Normalized field for filtering, routing, or downstream review. - `runSummary`: Run-level summary for counts, filters, charges, and next actions. ### Pricing This actor uses Apify pay-per-event pricing. Current public listing guidance: $29-$49 / 1,000 launch validation records until public data proof is complete. Charges are tied to buyer-visible value events such as `freshness-finding`, `dataset-processed`, `record-saved`, `enriched-record`. Small validation runs are supported so you can inspect output before scaling a schedule. - `freshness-finding`: Charge when Dataset Freshness SLA Monitoring produces Apify actor run or buyer-defined paid event. Typical price: $0.043. A run that produces 10 matching records charges only for the matched buyer-value events and remains capped by the run limit. - `dataset-processed`: Base charge when Dataset Freshness SLA Monitoring writes a non-empty default dataset. Typical price: $0.011. A run that produces 10 matching records charges only for the matched buyer-value events and remains capped by the run limit. - `record-saved`: Charge for each buyer-visible result saved by Dataset Freshness SLA Monitoring. Typical price: $0.003. A run that produces 10 matching records charges only for the matched buyer-value events and remains capped by the run limit. - `enriched-record`: Charge when Dataset Freshness SLA Monitoring adds match scoring, source evidence, or enrichment to a saved result. Typical price: $0.022. A run that produces 10 matching records charges only for the matched buyer-value events and remains capped by the run limit. - `first-run-cap`: Recommended first run budget cap. Typical price: $3.820. Start with the default small run, inspect the dataset, then raise maxItems or schedule recurring runs. ### API example ```bash curl -X POST "https://api.apify.com/v2/actors/zentrafoundry~dataset-freshness-sla-monitoring/runs" \ + -H "Authorization: Bearer $APIFY_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{"maxItems":10,"sourceIds":[],"includeSourceUrls":true,"includeMatchReasons":true,"outputMode":"buyer-ready-records"}' ```` ### Demo run ### Recommended first run ```json { "maxItems": 10, "sourceIds": [], "includeSourceUrls": true, "includeMatchReasons": true, "outputMode": "buyer-ready-records" } ``` ### Sample output Sample status: `sample_unavailable` at https://zentra.nimblique.studio/external/actor-review/samples/dataset-freshness-sla-monitoring.json. No fake sample is published; run a bounded real sample refresh before using examples in promotion. ### Recommended public tasks ```json [ { "name": "Review 10 tender notices", "description": "Low-cost validation run for checking buyer, deadline, category, and source fields.", "input": { "maxItems": 10, "sourceIds": [], "includeSourceUrls": true, "includeMatchReasons": true, "outputMode": "buyer-ready-records", "actorSlug": "dataset-freshness-sla-monitoring" } }, { "name": "Daily tender notice review", "description": "Recurring batch for new public procurement notices and changed opportunity details.", "schedule": "Daily during local business hours", "input": { "maxItems": 25, "sourceIds": [], "includeSourceUrls": true, "includeMatchReasons": true, "outputMode": "buyer-ready-records", "actorSlug": "dataset-freshness-sla-monitoring" } } ] ``` ### Example use cases - Track new dataset freshness sla monitoring opportunities without checking each tender source manually. - Route qualified records into CRM, spreadsheets, or internal review queues. - Monitor source links, tender details, and match reasons for faster triage. - Schedule recurring runs to catch new opportunities while keeping first-run costs low. ### Trust and compliance - Uses official tender portals, procurement notices, buyer pages, public documents, and configured source URLs. - Keeps source URLs and source identifiers in output records for auditability. - Does not require private credentials unless a source is explicitly configured for approved authenticated access. ### Reliability and QA - Prefilled Apify Store QA input runs in sample mode and should finish within the automated quality window. - Empty input is handled with deterministic sample or diagnostic output instead of a crash. - Demo/sample runs suppress buyer-value charges while still writing representative dataset rows. - Production runs use bounded `maxItems`, source references, warnings, and run summaries so blocked or changed targets are visible. ### Limitations - Results depend on public-source availability, source uptime, and source update cadence. - Public sources can revise records after publication; rerun scheduled tasks for fresh evidence. - Scores and match reasons are decision-support signals, not legal, financial, procurement, medical, safety, or regulatory advice. - Large production runs can cost more than the default smoke run; start small, inspect output, then scale schedules. ### Legal and responsible use Use this Actor only for public data or data you are authorized to process. You are responsible for complying with applicable laws, marketplace terms, robots policies, privacy rules, and source-specific limits. ### Support Open an issue on the Actor page with the run ID, input summary, expected result, and observed result. Do not include secrets, cookies, auth headers, or private account data. ### FAQ **Can I run this without URLs?** Yes. The default `sample` mode is designed to succeed without user-supplied URLs, and URL-backed runs can use `startUrls` when needed. **Can I schedule it?** Yes. Use `sinceLastRun`, `watchlistTerms`, and optional `webhookUrl` to turn the actor into a recurring alert or report workflow. **How do I verify value before scaling?** Run the recommended first-run input, review the sample output fields, then increase `maxItems` or schedule recurring runs after the dataset matches your use case. # Actor input Schema ## `sourceMode` (type: `string`): Choose whether to run the built-in sample or crawl supplied start URLs. ## `mode` (type: `string`): Use demo for Apify QA and first-time users, watchlist for production monitoring, or backfill for one-off historical collection. ## `startUrls` (type: `array`): Approved public source URLs for this Actor run. ## `maxItems` (type: `integer`): Upper bound for emitted records and pay-per-event charges. ## `lookbackDays` (type: `integer`): How far back to inspect source changes when the source supports date filtering. ## `baselineDatasetId` (type: `string`): Use this when comparing against a previous run or imported snapshot. ## `stateKey` (type: `string`): Optional named state bucket for recurring monitoring. Defaults to a stable hash of the watchlist. ## `sourceIds` (type: `array`): Optional source identifiers to include in the run. ## `pricingEvents` (type: `array`): Buyer-value events expected from this actor. ## `sinceLastRun` (type: `boolean`): Use Actor state to skip records whose dedupe keys were saved by earlier runs. ## `watchlistTerms` (type: `array`): Optional buyer terms used to explain matches and recurring monitoring. ## `webhookUrl` (type: `string`): Optional endpoint that receives a compact completion alert for scheduled runs. ## `includeSourceUrls` (type: `boolean`): Include source URLs in output records. ## `includeMatchReasons` (type: `boolean`): Include plain-language match reasons in output records. ## `includeRaw` (type: `boolean`): Include compact raw source fragments when supported. Disabled by default for readable output. ## `emitDiagnostics` (type: `boolean`): Emit a transparent diagnostic item in demo mode if the live official source is temporarily unavailable. ## `outputMode` (type: `string`): Shape of returned records. ## `actorSlug` (type: `string`): Zentra actor slug used for review and traceability. ## Actor input object example ```json { "sourceMode": "sample", "mode": "demo", "startUrls": [ { "url": "https://ted.europa.eu/en/", "sourceId": "TED-SEARCH", "userData": { "sourceId": "TED-SEARCH" } } ], "maxItems": 10, "lookbackDays": 3650, "sourceIds": [], "pricingEvents": [], "sinceLastRun": false, "watchlistTerms": [], "includeSourceUrls": true, "includeMatchReasons": true, "includeRaw": false, "emitDiagnostics": true, "outputMode": "sample-records" } ``` # Actor output Schema ## `results` (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 = { "sourceMode": "sample", "mode": "demo", "startUrls": [ { "url": "https://ted.europa.eu/en/", "sourceId": "TED-SEARCH", "userData": { "sourceId": "TED-SEARCH" } } ], "maxItems": 10, "lookbackDays": 3650, "includeRaw": false, "emitDiagnostics": true, "outputMode": "sample-records" }; // Run the Actor and wait for it to finish const run = await client.actor("zentrafoundry/dataset-freshness-sla-monitoring").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 = { "sourceMode": "sample", "mode": "demo", "startUrls": [{ "url": "https://ted.europa.eu/en/", "sourceId": "TED-SEARCH", "userData": { "sourceId": "TED-SEARCH" }, }], "maxItems": 10, "lookbackDays": 3650, "includeRaw": False, "emitDiagnostics": True, "outputMode": "sample-records", } # Run the Actor and wait for it to finish run = client.actor("zentrafoundry/dataset-freshness-sla-monitoring").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 '{ "sourceMode": "sample", "mode": "demo", "startUrls": [ { "url": "https://ted.europa.eu/en/", "sourceId": "TED-SEARCH", "userData": { "sourceId": "TED-SEARCH" } } ], "maxItems": 10, "lookbackDays": 3650, "includeRaw": false, "emitDiagnostics": true, "outputMode": "sample-records" }' | apify call zentrafoundry/dataset-freshness-sla-monitoring --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=zentrafoundry/dataset-freshness-sla-monitoring", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Dataset Freshness SLA Monitoring", "description": "Monitor official tender portals, procurement notices, buyer pages, public documents, and configured source URLs and return tender notice records with buyer, deadline, category, source URL, and action-ready context.", "version": "1.0", "x-build-id": "hBhbgI49hh44jQ2Is" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/zentrafoundry~dataset-freshness-sla-monitoring/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-zentrafoundry-dataset-freshness-sla-monitoring", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/zentrafoundry~dataset-freshness-sla-monitoring/runs": { "post": { "operationId": "runs-sync-zentrafoundry-dataset-freshness-sla-monitoring", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/zentrafoundry~dataset-freshness-sla-monitoring/run-sync": { "post": { "operationId": "run-sync-zentrafoundry-dataset-freshness-sla-monitoring", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "sourceMode": { "title": "Source mode", "enum": [ "startUrls", "sample" ], "type": "string", "description": "Choose whether to run the built-in sample or crawl supplied start URLs.", "default": "sample" }, "mode": { "title": "Run mode", "enum": [ "demo", "watchlist", "backfill" ], "type": "string", "description": "Use demo for Apify QA and first-time users, watchlist for production monitoring, or backfill for one-off historical collection.", "default": "watchlist" }, "startUrls": { "title": "Start URLs", "type": "array", "description": "Approved public source URLs for this Actor run.", "default": [ { "url": "https://ted.europa.eu/en/", "sourceId": "TED-SEARCH", "userData": { "sourceId": "TED-SEARCH" } } ], "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxItems": { "title": "Maximum items", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Upper bound for emitted records and pay-per-event charges.", "default": 1 }, "lookbackDays": { "title": "Lookback window in days", "minimum": 1, "maximum": 3650, "type": "integer", "description": "How far back to inspect source changes when the source supports date filtering.", "default": 30 }, "baselineDatasetId": { "title": "Optional baseline dataset ID", "type": "string", "description": "Use this when comparing against a previous run or imported snapshot." }, "stateKey": { "title": "Snapshot state key", "type": "string", "description": "Optional named state bucket for recurring monitoring. Defaults to a stable hash of the watchlist." }, "sourceIds": { "title": "Source IDs", "type": "array", "description": "Optional source identifiers to include in the run.", "default": [], "items": { "type": "string" } }, "pricingEvents": { "title": "Pricing events", "type": "array", "description": "Buyer-value events expected from this actor.", "default": [], "items": { "type": "string" } }, "sinceLastRun": { "title": "Only new records", "type": "boolean", "description": "Use Actor state to skip records whose dedupe keys were saved by earlier runs.", "default": false }, "watchlistTerms": { "title": "Watchlist terms", "type": "array", "description": "Optional buyer terms used to explain matches and recurring monitoring.", "default": [], "items": { "type": "string" } }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "Optional endpoint that receives a compact completion alert for scheduled runs." }, "includeSourceUrls": { "title": "Include source URLs", "type": "boolean", "description": "Include source URLs in output records.", "default": true }, "includeMatchReasons": { "title": "Include match reasons", "type": "boolean", "description": "Include plain-language match reasons in output records.", "default": true }, "includeRaw": { "title": "Include raw source fragments", "type": "boolean", "description": "Include compact raw source fragments when supported. Disabled by default for readable output.", "default": false }, "emitDiagnostics": { "title": "Emit diagnostic fallback", "type": "boolean", "description": "Emit a transparent diagnostic item in demo mode if the live official source is temporarily unavailable.", "default": true }, "outputMode": { "title": "Output mode", "enum": [ "sample-records", "buyer-ready-records" ], "type": "string", "description": "Shape of returned records.", "default": "sample-records" }, "actorSlug": { "title": "Actor slug", "type": "string", "description": "Zentra actor slug used for review and traceability." } } }, "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 } } } } } } } } } } ```