# Actor Dataset Schema Generator (`junipr/actor-dataset-schema-generator`) Actor Audit actor dataset schema generator inputs and return structured findings, evidence rows, and a buyer-ready report for SEO, developer, and operations teams. - **URL**: https://apify.com/junipr/actor-dataset-schema-generator.md - **Developed by:** [junipr](https://apify.com/junipr) (community) - **Categories:** SEO tools, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.90 / 1,000 item checkeds 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Actor Dataset Schema Generator ### Store Positioning **Store title:** Actor Dataset Schema Generator **Short description:** Audit actor dataset schema generator inputs and return structured findings, evidence rows, and a buyer-ready report for SEO, developer, and operations teams. **SEO title:** Actor Dataset Schema Generator — API, schema, and developer QA **SEO description:** Audit actor dataset schema generator inputs and return structured findings, evidence rows, and a buyer-ready report for SEO, developer, and operations teams. Use it to catch contract drift, schema mistakes, unsafe endpoint assumptions, and developer-tool quality issues before release. **Categories:** SEO_TOOLS, DEVELOPER_TOOLS **Keywords:** actor, dataset, schema, generator, apify actor, data qa, dataset qa, api testing, api/developer qa ### Fixed-Inclusive PPE Pricing This actor uses pay-per-event pricing. Event prices include Apify platform usage; users are not expected to pay a separate platform-usage pass-through charge for the configured pricing model. - Tier: A1 — API/developer QA - Primary event: `schema-validated` at $0.00650 base - Default max charge: $10.00 - Store discounts: FREE/BRONZE base, SILVER discounted, GOLD deepest approved discount Event set: - `actor-start`: base $0.00500, GOLD $0.00400. Actor Dataset Schema Generator: charged when actor start is completed. The price includes Apify platform usage; no separate usage pass-through is intended. - `schema-validated`: base $0.00650, GOLD $0.00520. Actor Dataset Schema Generator: charged when schema validated is completed. The price includes Apify platform usage; no separate usage pass-through is intended. - `contract-rule-checked`: base $0.00390, GOLD $0.00312. Actor Dataset Schema Generator: charged when contract rule checked is completed. The price includes Apify platform usage; no separate usage pass-through is intended. - `report-generated`: base $0.05000, GOLD $0.04000. Actor Dataset Schema Generator: charged when report generated is completed. The price includes Apify platform usage; no separate usage pass-through is intended. ### Public Task Concepts - Audit Actor Dataset Schema controls on a capped public sample - Find high-priority Actor Dataset Schema issues before release - Validate Actor Dataset Schema evidence from supplied pages - Prioritize Actor Dataset Schema fixes with severity and proof - Export Actor Dataset Schema QA rows for client review Generate or improve Apify dataset schemas/views from sample actor output rows. ### What This Actor Does Actor Dataset Schema Generator turns explicit watchlists or supplied records into structured Apify dataset rows and a concise report. The capped built-in records make the default run deterministic and avoid network, browser, proxy, LLM, or third-party API costs. The actor checks each supplied record for source identity, matched watch terms, actor-specific metadata, issue signals, severity, recommendations, and pricing metadata. It writes one dataset row per processed record and, when enabled, key-value-store JSON and Markdown summaries for operational workflows. ### What This Actor Does Not Do - It does not provide legal, medical, financial, investment, safety, or compliance advice. - It does not guarantee completeness of any public source or third-party dataset. - It does not collect sensitive personal data by default. - It does not rely on unsupported private APIs. ### Best Use Cases - schema drafting from dataset rows - Apify output view preparation - dataset contract cleanup ### Input Fields Explained | Field | Type | Default | Notes | | --- | --- | --- | --- | | `records` | array | two built-in records | Records or snapshots to check. Each item can include title, description, body, entity, category, keywords, and metadata. | | `urls` | array | [] | Reserved for optional source URLs. Defaults do not fetch URLs. | | `fetchUrls` | boolean | false | Enables HTTP fetching for records with sourceUrl and no body. Keep false when records already contain the text to analyze. | | `watchTerms` | array | actor defaults | Terms used to mark relevant records and alerts. | | `includeReport` | boolean | true | Writes JSON and Markdown report artifacts to the key-value store. | | `maxItems` | integer | 2 | Caps processed records. Maximum is 50. | | `maxTextBytes` | integer | 500000 | Caps analyzed text per record. | | `debug` | boolean | false | Enables debug logging. | ### Example Input ```json { "records": [ { "sourceId": "schema-sample", "sourceUrl": "dataset://sample", "title": "Dataset sample rows", "description": "Two sample actor output rows for schema inference.", "entityName": "sample-actor", "category": "schema", "keywords": [ "schema" ], "metadata": { "sampleRows": [ { "url": "https://example.com/a", "status": "pass", "score": 98, "tags": [ "seo" ] }, { "url": "https://example.com/b", "status": "warn", "score": 62, "tags": [ "missing-alt" ] } ] } }, { "sourceId": "schema-empty", "sourceUrl": "dataset://empty", "title": "Missing sample rows", "description": "Schema request without sample rows.", "entityName": "sample-actor", "category": "schema", "keywords": [], "metadata": {} } ], "includeReport": true, "maxItems": 2 } ```` ### Output Fields Explained | Field | Meaning | | --- | --- | | `auditId` | Stable hash for the checked record. | | `sourceId`, `sourceUrl` | Source identifiers for traceability. | | `status`, `severity`, `score` | Normalized outcome and 0-100 quality/risk score. | | `matchedKeywords` | Watch terms found in the record text or metadata. | | `issues`, `issueCodes` | Actor-specific findings with severity and messages. | | `recommendations` | Next actions for the operator or content/data owner. | | `metadata` | Original metadata plus derived helper fields when applicable. | | `pricingTemplate`, `pricingEventName` | Pricing source and primary PPE event used by the actor. | ### Example Output ```json { "auditId": "actorda_2445f7caa4a09be9", "actorSlug": "actor-dataset-schema-generator", "actorName": "Actor Dataset Schema Generator", "sourceType": "fixture", "sourceId": "schema-sample", "sourceUrl": "dataset://sample", "title": "Dataset sample rows", "status": "pass", "severity": "low", "score": 100, "matchedKeywords": [ "dataset", "sample rows", "schema" ], "primaryEntity": "sample-actor", "category": "schema", "publishedAt": null, "summary": "Two sample actor output rows for schema inference.", "issueCount": 1, "issues": [ { "code": "schema-generated", "severity": "info", "message": "Sample rows are available for schema generation.", "recommendation": "Review the generated schema descriptions before publishing." } ], "issueCodes": [ "schema-generated" ], "recommendations": [ "Review the generated schema descriptions before publishing." ], "metadata": { "sampleRows": [ { "url": "https://example.com/a", "status": "pass", "score": 98, "tags": [ "seo" ] }, { "url": "https://example.com/b", "status": "warn", "score": 62, "tags": [ "missing-alt" ] } ], "generatedDatasetSchema": { "actorDatasetSchemaVersion": 1, "title": "Generated Dataset Schema", "description": "Inferred from supplied sample rows. Review descriptions before publishing.", "type": "object", "properties": { "score": { "type": "number", "description": "Inferred field \"score\" from sample dataset rows." }, "status": { "type": "string", "description": "Inferred field \"status\" from sample dataset rows." }, "tags": { "type": "array", "description": "Inferred field \"tags\" from sample dataset rows.", "items": { "type": "string" } }, "url": { "type": "string", "description": "Inferred field \"url\" from sample dataset rows." } } }, "sourceSpecific": { "structuredRows": [ { "url": "https://example.com/a", "status": "pass", "score": 98, "tags": [ "seo" ] }, { "url": "https://example.com/b", "status": "warn", "score": 62, "tags": [ "missing-alt" ] } ] }, "normalizedTextPreview": "schema-sample dataset://sample Dataset sample rows Two sample actor output rows for schema inference. sample-actor schema schema {\"sampleRows\":[{\"url\":\"https://example.com/a\",\"status\":\"pass\",\"score\":98,\"tags\":[\"seo\"]},{\"url\":\"https://examp" }, "textBytes": 302, "checkedAt": "2026-07-02T00:00:00.000Z", "pricingTemplate": "A1 — API/developer QA", "pricingEventName": "schema-validated" } ``` ### Cost-Control Tips - Keep `maxItems` small for default and scheduled checks. - Use supplied records or snapshots for deterministic QA runs. - Leave `fetchUrls` off unless live source fetching is explicitly needed. - Review warning rows before increasing caps. - Schedule small frequent runs instead of broad one-off sweeps. ### Scheduling Examples - Daily watchlist check with two to five high-value records. - Weekly QA run before publishing a site, data, or actor update. - Post-migration or post-release validation using a saved record set. ### Public Task Examples - `tiny-default-check` - Run a two-record actor dataset schema generator check with report artifacts enabled. - `watchlist-digest` - Process a small supplied watchlist and summarize matched terms. - `risk-triage-report` - Return only records with warning or fail status for operator follow-up. - `scheduled-monitor` - Run Actor Dataset Schema Generator on a daily or weekly schedule with tight caps. - `schema-and-billing-preflight` - Validate output shape and PPE event names before live setup. ### FAQ #### Can I schedule recurring checks? Yes. Save a capped input and use Apify schedules to run it daily or weekly. #### Can I use this with live URLs? Yes. Set `fetchUrls` to true and provide `urls` or records with `sourceUrl`. Failed fetches produce explicit diagnostic rows. #### Why does it charge before pushing output? Pay-per-event actors must charge before paid output is written. If the charge limit is reached, processing stops before another paid row is emitted. #### What happens on charge limits? The actor stops gracefully and writes a summary instead of pushing unpaid result rows. #### Is this legal, financial, safety, or compliance advice? No. The actor produces operational signals and structured data for review. Human owners remain responsible for decisions. ### Troubleshooting - If the dataset is empty, check that `maxItems` is greater than zero and that records are supplied or defaults are enabled. - If a record is flagged unexpectedly, inspect `issueCodes`, `matchedKeywords`, and the normalized metadata preview. - If live fetching fails, rerun with supplied record bodies or snapshots and keep `fetchUrls` false. ### Limitations This actor only sees the records, snapshots, or URLs supplied to it. It does not guarantee complete public-source coverage, private-source access, or all edge-case parsing. Browser and proxy flows are not used by default. ### Source And Safety Notes The built-in records are labeled through `sourceType: "fixture"` and are intended to show the output contract. When connecting public sources, use official or permissioned endpoints where possible and avoid sensitive personal data. Do not use the output as a substitute for professional advice. ### Changelog - 2026-07-02: Initial actor package with schemas, capped defaults, pricing controls, and runtime validation. # Actor input Schema ## `records` (type: `array`): Records, snapshots, or source observations to process. Defaults to two capped built-in records. ## `urls` (type: `array`): Optional URLs reserved for live fetching when Fetch URLs is true. ## `fetchUrls` (type: `boolean`): Fetch sourceUrl text over HTTP. Keep false when records already contain the text to analyze. ## `watchTerms` (type: `array`): Keywords or topics used to flag relevant records. ## `includeReport` (type: `boolean`): Write JSON and Markdown report artifacts to the key-value store. ## `maxItems` (type: `integer`): Maximum records to process in one run. ## `maxTextBytes` (type: `integer`): Maximum searchable text bytes per record. ## `dryRun` (type: `boolean`): Validate input and write a dry-run summary without PPE charges or dataset output. ## `debug` (type: `boolean`): Enable debug logging. ## Actor input object example ```json { "records": [ { "sourceId": "schema-sample", "sourceUrl": "dataset://sample", "title": "Dataset sample rows", "description": "Two sample actor output rows for schema inference.", "entityName": "sample-actor", "category": "schema", "keywords": [ "schema" ], "metadata": { "sampleRows": [ { "url": "https://example.com/a", "status": "pass", "score": 98, "tags": [ "seo" ] }, { "url": "https://example.com/b", "status": "warn", "score": 62, "tags": [ "missing-alt" ] } ] } }, { "sourceId": "schema-empty", "sourceUrl": "dataset://empty", "title": "Missing sample rows", "description": "Schema request without sample rows.", "entityName": "sample-actor", "category": "schema", "keywords": [], "metadata": {} } ], "urls": [], "fetchUrls": false, "watchTerms": [ "schema", "dataset", "sample rows" ], "includeReport": true, "maxItems": 2, "maxTextBytes": 500000, "dryRun": false, "debug": false } ``` # Actor output Schema ## `results` (type: `string`): Structured result rows in the default dataset. ## `summary` (type: `string`): Summary JSON in the key-value store when includeReport is enabled. ## `markdownReport` (type: `string`): Markdown report in the key-value store when includeReport is enabled. # 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("junipr/actor-dataset-schema-generator").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("junipr/actor-dataset-schema-generator").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 junipr/actor-dataset-schema-generator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=junipr/actor-dataset-schema-generator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Actor Dataset Schema Generator", "description": "Audit actor dataset schema generator inputs and return structured findings, evidence rows, and a buyer-ready report for SEO, developer, and operations teams.", "version": "1.0", "x-build-id": "OkR2NK6b39kTF9TQY" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/junipr~actor-dataset-schema-generator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-junipr-actor-dataset-schema-generator", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/junipr~actor-dataset-schema-generator/runs": { "post": { "operationId": "runs-sync-junipr-actor-dataset-schema-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/junipr~actor-dataset-schema-generator/run-sync": { "post": { "operationId": "run-sync-junipr-actor-dataset-schema-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "records": { "title": "Records To Check", "type": "array", "description": "Records, snapshots, or source observations to process. Defaults to two capped built-in records.", "items": { "type": "object" }, "default": [ { "sourceId": "schema-sample", "sourceUrl": "dataset://sample", "title": "Dataset sample rows", "description": "Two sample actor output rows for schema inference.", "entityName": "sample-actor", "category": "schema", "keywords": [ "schema" ], "metadata": { "sampleRows": [ { "url": "https://example.com/a", "status": "pass", "score": 98, "tags": [ "seo" ] }, { "url": "https://example.com/b", "status": "warn", "score": 62, "tags": [ "missing-alt" ] } ] } }, { "sourceId": "schema-empty", "sourceUrl": "dataset://empty", "title": "Missing sample rows", "description": "Schema request without sample rows.", "entityName": "sample-actor", "category": "schema", "keywords": [], "metadata": {} } ] }, "urls": { "title": "URLs To Fetch", "type": "array", "description": "Optional URLs reserved for live fetching when Fetch URLs is true.", "items": { "type": "string" }, "default": [] }, "fetchUrls": { "title": "Fetch URLs", "type": "boolean", "description": "Fetch sourceUrl text over HTTP. Keep false when records already contain the text to analyze.", "default": false }, "watchTerms": { "title": "Watch Terms", "type": "array", "description": "Keywords or topics used to flag relevant records.", "items": { "type": "string" }, "default": [ "schema", "dataset", "sample rows" ] }, "includeReport": { "title": "Include Report Artifacts", "type": "boolean", "description": "Write JSON and Markdown report artifacts to the key-value store.", "default": true }, "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 50, "type": "integer", "description": "Maximum records to process in one run.", "default": 2 }, "maxTextBytes": { "title": "Max Text Bytes", "minimum": 100, "maximum": 2000000, "type": "integer", "description": "Maximum searchable text bytes per record.", "default": 500000 }, "dryRun": { "title": "Dry Run", "type": "boolean", "description": "Validate input and write a dry-run summary without PPE charges or dataset output.", "default": false }, "debug": { "title": "Debug Logs", "type": "boolean", "description": "Enable debug logging.", "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 } } } } } } } } } } ```