# ISBN Decoder API (`s-r/isbn-decoder`) Actor Resolve any book title and/or author into every published edition with canonical ISBN-13, matching ISBN-10, retailer source attribution, and a confidence tier. Harvested from Amazon, Goodreads, OpenLibrary, WorldCat, Bol, AbeBooks. - **URL**: https://apify.com/s-r/isbn-decoder.md - **Developed by:** [SR](https://apify.com/s-r) (community) - **Categories:** Developer tools, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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. 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 ## ISBN Decoder API — by ISBN, OLID, title, or author A book ISBN lookup API that takes whatever bibliographic input you have — a 13-digit ISBN, an Open Library identifier (`OL29832788M`), a book title, or an author name — and returns the canonical edition(s) with publisher, year, page count, language, cover, and Goodreads rating. Stitched from three free, no-auth, no-captcha public sources (Open Library, Goodreads autocomplete, Google SERP retailer harvest) routed through one of four input-shaped paths. Different inputs take different paths automatically: - **ISBN input** → direct Open Library lookup (Path A — captcha-immune, 1-8s) - **OLID input** → direct Open Library by OLID (Path B — single API hit) - **Title or author** → Google SERP retailer harvest (Path C) → Open Library search rescue if captchas (Path D) - **Every path** → Goodreads autocomplete runs in parallel for the canonical "what you probably meant" hint with cover + rating One actor call returns up to 10 editions for popular books, grouped per ISBN-13, deduplicated across retailers, ranked by source count, enriched with Open Library bibliographic data. ### What you get - **Multi-edition coverage** — paperback, hardcover, deluxe, audiobook, regional translations all surface as separate editions with their own ISBN-13 - **Both identifiers** — ISBN-13 (canonical, checksum-validated) and the matching ISBN-10 (auto-converted from 978-prefix ISBN-13; null for 979-prefix audiobooks) - **Open Library bibliographic data** — publisher, publish_date (raw), publish_year (extracted), pages, openlibrary_id, languages (ISO 639-3) - **Cleaned title** — extracted from the strongest retailer snippet, with format suffixes and retailer-name tails stripped - **Normalized author** — resolved via Open Library's author key chase or extracted from SERP retailer patterns - **Source attribution** — bare hostnames of every retailer + Open Library + Goodreads that confirmed the ISBN - **Confidence tier** — `high` when 2+ independent sources confirm; `medium` for single-source hits - **Goodreads top hint** — separate per-run field with title/author/cover/rating/ratings_count from Goodreads autocomplete - **Path attribution** — `path: A|B|C|D` tells you which route resolved each edition ### Why use this book ISBN lookup Most ISBN lookup APIs accept only one input shape. Either you have an ISBN and want the metadata, OR you have a title and want the ISBNs. This actor routes intelligently based on input shape — feed it whatever you have. The official ISBN registry at `isbn-international.org` is paid. Bowker's Books in Print is enterprise pricing. Open Library's direct API requires you to write the dispatch logic yourself (ISBN vs OLID vs search) and doesn't include cross-retailer SERP harvesting. Library APIs (Library of Congress, OpenLibrary direct) need OAuth for catalogs and miss every retail edition. Naive retailer scraping breaks on the 30% of book pages that render ISBNs as images. This actor stitches Open Library's free no-auth API, Goodreads' autocomplete endpoint, and Google SERP retailer-page harvest into a single call with auto-dispatch. The Path D fallback handles SERP captcha — when Google blocks the retailer harvest, OpenLibrary search rescues automatically and the caller sees editions either way. ### Input | Field | Type | Required | Default | Notes | |---|---|---|---|---| | `title` | string | one-of | `Sapiens` | Book title (wrapped in quotes for exact-match SERP) | | `author` | string | one-of | `Yuval Noah Harari` | Author name (wrapped in quotes); combine with title for best precision | | `q` | string | one-of | — | Free-form query. If valid ISBN-10/13, short-circuits to Path A (direct OL) | | `olid` | string | one-of | — | Open Library identifier like `OL29832788M` or full URL like `https://openlibrary.org/books/OL29832788M/Beach_Read`. Short-circuits to Path B | | `country` | string | no | `us` | Google geo (gl). Affects which Amazon TLDs dominate the SERP on Path C | | `language` | string | no | `en` | Google UI language (hl) | | `enrich` | boolean | no | `true` | When false, skip Goodreads autocomplete and OL per-edition enrichment. Restores legacy SERP-only behavior | | `hits` | boolean | no | `false` | Include raw SERP results in the first dataset item for debugging | At least one of `title`, `author`, `q`, or `olid` is required. ### Output One dataset item per edition. Multi-edition books produce multiple items per run. Example for `title=Sapiens, author=Yuval Noah Harari` (Path C): ```json { "query": {"title": "Sapiens", "author": "Yuval Noah Harari"}, "country": "us", "language": "en", "path": "C", "title": "Sapiens: A Brief History of Humankind", "author": "Yuval Noah Harari", "isbn13": "9780062316097", "isbn10": "0062316095", "publisher": "Harper", "publish_date": "2015", "publish_year": 2015, "pages": 443, "openlibrary_id": "OL27000666M", "languages": ["eng"], "sources": ["amazon.com", "openlibrary.org"], "source_count": 2, "confidence": "high", "goodreads_top": { "title": "Sapiens: A Brief History of Humankind", "author": "Yuval Noah Harari", "book_id": "23692271", "book_url": "https://www.goodreads.com/book/show/23692271-sapiens", "cover_url": "https://i.gr-assets.com/.../23692271._SX50_.jpg", "pages": 443, "avg_rating": "4.41", "ratings_count": 893421 }, "edition_rank": 1, "total_results": 10, "duration_seconds": 4.39, "errors": [] } ```` `goodreads_top` is denormalized on every edition item in the same run (Apify pattern — one record per dataset row). The Goodreads hint represents "what Goodreads thinks the query was about" and is NOT joined to specific ISBNs — it's diagnostic, not authoritative. ### Path dispatch — how input shape decides the route ``` ┌───────────────────────────────────────┐ │ q / title contains a valid ISBN? │── yes ─► PATH A └─────────────────┬─────────────────────┘ direct OL by ISBN │ no + Goodreads (parallel) ▼ ┌───────────────────────────────────────┐ │ olid contains a valid OL...M token? │── yes ─► PATH B └─────────────────┬─────────────────────┘ direct OL by OLID │ no + author chase ▼ ┌───────────────────────────────────────┐ │ title and/or author provided? │── yes ─► PATH C └─────────────────┬─────────────────────┘ Google SERP + Goodreads │ + OL per-edition enrichment │ │ │ │ 0 editions? │ ▼ │ PATH D │ Open Library search rescue │ no ▼ HTTP 400 ``` ### Use cases **Book inventory and catalog systems.** When a librarian or used-bookstore operator scans a barcode that returns ISBN-10 only (pre-2007 books), pass it as `q=` to hit Path A. The actor validates the check digit, converts to ISBN-13, fetches the Open Library record, resolves the author key chain, and returns publisher + year + pages + language. One call replaces N retailer-specific lookups. **Author bibliography enrichment.** For a book-blog or review-site backend, call with `author=Stephen King` for Path C → D. Path C tries Google SERP for retailer-indexed ISBNs; if SERP captchas, Path D rescues with Open Library search and returns 8-10 editions with publisher/year/pages already filled in. The Amazon URL-slug fallback recovers titles for SERP results where Google truncated the snippet. **Cross-locale price comparison.** A Dutch-language book might be priced at €19.95 on Bol.com but €14.50 on Amazon.nl. To compare, you first need the same ISBN-13 across both retailers. This actor's per-edition `sources` array explicitly tells you which retailer carries which ISBN — feed the resulting ISBN-13 to your price-comparison logic. For Dutch books, call with `country=nl&language=nl` so Bol-dominated results dominate the Path C SERP. **Edition validation from a publisher feed.** When a publisher sends you a list of ISBNs and you want to confirm each one resolves to a real cataloged book, pass each `q=` to Path A. The actor returns the canonical edition with publisher/year/pages, OR fails soft with `path=A, isbn13=null` for unmapped ISBNs. The `confidence` field tells you whether multiple sources agreed. ### How it compares | Source | Coverage | Inputs accepted | Auth | |---|---|---|---| | **ISBN-International registry** | Authoritative; every published book | ISBN only | Paid subscription | | **Open Library direct API** | Library-catalogued | ISBN, OLID, search | None, but you write the dispatch | | **Goodreads autocomplete direct** | Goodreads-indexed | Free text only | None, but no ISBN in response | | **Retailer-specific scrapers** | One retailer at a time | Search query | Often blocked / rate-limited | | **This actor** | Open Library + Goodreads + 10 retailers via SERP, auto-dispatched | ISBN, OLID, title, author, free text | None | This actor is for when you want INPUT-FLEXIBLE bibliographic resolution. The four-path dispatch means callers don't pre-classify inputs; you can pipe a barcode scan, a fuzzy title, or an OL identifier into the same endpoint. ### Pricing Pay-per-event — one charge per edition successfully extracted. Path A returns 1 edition for 1 charge. Path C can return 3-10 editions, each charged separately. A run with zero matches (bad ISBN, captcha + no rescue) charges only the symbolic actor-start fee. No actor-start fee on result-only billing. ### Limits and gotchas - **Synthetic / invalid ISBNs** fall through to SERP (Path C). The ISBN detector validates check digits before short-circuiting to Path A — bad check digits don't pollute the captcha-immune path with wrong matches. - **Localized first edition** — Open Library's `isbn` array per record is not language-sorted, so the FIRST valid ISBN for `title=Sapiens` may be the French (Albin Michel 2022), Polish, or Thai edition rather than the English original. The `languages` field tells you which. - **Goodreads ≠ editions.** `goodreads_top` represents what Goodreads' autocomplete returned for the query string. It is NOT joined to specific ISBNs — never assume `goodreads_top.title` describes any particular edition in the dataset. They can disagree (and that's diagnostic, not a bug). - **SERP captcha** — Google captchas the underlying `/serp` endpoint fairly often. Path D (OL search rescue) handles this transparently but adds 15-25s of latency. - **Path C OL enrichment is fail-soft** — when Open Library returns 5xx for a specific ISBN, that edition keeps its SERP-extracted title/author but loses the publisher/year/pages enrichment. Other editions in the same run are unaffected. - **AbeBooks titles drop hyphens** — `Designing Data-Intensive Applications` becomes `Designing Data Intensive Applications`. The source-rank algorithm correctly prefers Amazon's cleaner snippet when both retailers surface the same ISBN. - **979-prefix audiobooks have no ISBN-10.** The conversion only works for 978-prefix codes. Customers correctly see `isbn10: null` for audiobook-only editions. - **OLID input bypasses Goodreads** when no title is resolved. Path B short-circuits to OL alone; Goodreads only fires after the OL record's title is known. ### FAQ **Can I look up an ISBN without an API key?** Yes. Pass `q=9780374533557` and the actor takes Path A — direct Open Library lookup, no captchas, no third-party keys. Returns 1 edition with publisher/year/pages from OL plus the Goodreads autocomplete hint. **Does it work for Open Library identifiers?** Yes — pass `olid=OL29832788M` (bare) or `olid=https://openlibrary.org/books/OL29832788M/Beach_Read` (full URL). Both forms work via Path B's regex extractor. **What happens when Google captchas the SERP path?** Path D auto-rescues. The actor calls Open Library's `/search.json` with title + author, picks the first valid ISBN-13 from each returned work, and emits the editions with `path=D`. Total latency goes from 4-10s (Path C happy) to 18-35s (C → D rescue). The caller sees editions either way. **Why is `author` null on some Path A returns?** Open Library stores authors as key references (`/authors/OL7410858A`). The actor chases the first author key into `/authors/{KEY}.json` to resolve the name. When OL returns 5xx for the author endpoint (which happens occasionally), the field stays null. The publisher/year/pages still populate from the main record. **What's the difference between Path C and Path D?** Path C harvests ISBNs from retailer-indexed Google SERP results (Amazon, Goodreads, Bol, AbeBooks, etc.) and cross-confirms across multiple sources. Path D bypasses Google entirely and searches Open Library directly — fewer cross-source confirmations but immune to captchas. The actor picks C first and rescues with D only when C returns zero editions. **How do I get just the SERP data without Open Library enrichment?** Pass `enrich=false`. This restores the legacy SERP-only behavior — Path C runs without the Open Library per-edition fan-out and without Goodreads autocomplete. Faster (~80ms cached) but no publisher/year/pages/languages fields populated. ### Related Actors - [VIN Decoder](https://apify.com/s-r/vin-decoder) — decode any 17-character VIN into year/make/model + recalls + safety - [Phone Number Decoder](https://apify.com/s-r/phone-number-decoder) — find every business phone for any domain - [Backlinks Checker](https://apify.com/s-r/backlinks-checker) — domain link profile and per-link records # Actor input Schema ## `title` (type: `string`): Book title (wrapped in quotes for exact-match search). At least one of title / author / q / olid is required. ## `author` (type: `string`): Author name (wrapped in quotes). Combine with title for best precision. ## `q` (type: `string`): Free-form query — accepts ISBN-10, ISBN-13, or a title. If it's a valid ISBN it short-circuits to the direct Open Library path (captcha-immune, ~1-4s). ## `olid` (type: `string`): Open Library identifier like OL29832788M, or a full URL such as https://openlibrary.org/books/OL29832788M/Beach\_Read. Short-circuits SERP and Goodreads. ## `country` (type: `string`): Google geo. Affects which Amazon TLDs dominate the SERP. Default: us ## `language` (type: `string`): Google UI language. Default: en ## `enrich` (type: `boolean`): When true (default), fan out Open Library per-edition enrichment and Goodreads autocomplete in parallel. Set false to restore SERP-only behavior. ## `hits` (type: `boolean`): Include raw SERP results in the first dataset item for debugging extraction misses. ## Actor input object example ```json { "title": "Sapiens", "author": "Yuval Noah Harari", "country": "us", "language": "en", "enrich": true, "hits": false } ``` # 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 = { "title": "Sapiens", "author": "Yuval Noah Harari" }; // Run the Actor and wait for it to finish const run = await client.actor("s-r/isbn-decoder").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 = { "title": "Sapiens", "author": "Yuval Noah Harari", } # Run the Actor and wait for it to finish run = client.actor("s-r/isbn-decoder").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 '{ "title": "Sapiens", "author": "Yuval Noah Harari" }' | apify call s-r/isbn-decoder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=s-r/isbn-decoder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ISBN Decoder API", "description": "Resolve any book title and/or author into every published edition with canonical ISBN-13, matching ISBN-10, retailer source attribution, and a confidence tier. Harvested from Amazon, Goodreads, OpenLibrary, WorldCat, Bol, AbeBooks.", "version": "0.1", "x-build-id": "i0NaGgcT04kQjW20L" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/s-r~isbn-decoder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-s-r-isbn-decoder", "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/s-r~isbn-decoder/runs": { "post": { "operationId": "runs-sync-s-r-isbn-decoder", "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/s-r~isbn-decoder/run-sync": { "post": { "operationId": "run-sync-s-r-isbn-decoder", "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": [ "title" ], "properties": { "title": { "title": "Book title", "type": "string", "description": "Book title (wrapped in quotes for exact-match search). At least one of title / author / q / olid is required." }, "author": { "title": "Author name", "type": "string", "description": "Author name (wrapped in quotes). Combine with title for best precision." }, "q": { "title": "Free-form query (fallback)", "type": "string", "description": "Free-form query — accepts ISBN-10, ISBN-13, or a title. If it's a valid ISBN it short-circuits to the direct Open Library path (captcha-immune, ~1-4s)." }, "olid": { "title": "Open Library ID", "type": "string", "description": "Open Library identifier like OL29832788M, or a full URL such as https://openlibrary.org/books/OL29832788M/Beach_Read. Short-circuits SERP and Goodreads." }, "country": { "title": "Country (gl)", "type": "string", "description": "Google geo. Affects which Amazon TLDs dominate the SERP. Default: us", "default": "us" }, "language": { "title": "Language (hl)", "type": "string", "description": "Google UI language. Default: en", "default": "en" }, "enrich": { "title": "Enrich with Open Library + Goodreads", "type": "boolean", "description": "When true (default), fan out Open Library per-edition enrichment and Goodreads autocomplete in parallel. Set false to restore SERP-only behavior.", "default": true }, "hits": { "title": "Include raw SERP hits", "type": "boolean", "description": "Include raw SERP results in the first dataset item for debugging extraction misses.", "default": false } } }, "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 } } } } } } } } } } ```