{ "openapi": "3.0.1", "info": { "title": "Apify Actor Discoverability & SEO Audit", "description": "Audit an Apify Actor for discoverability by LLM agents and Apify Store ranking. Scores description quality, schema, disambiguation, agentic-pay, and live agent-search retrieval across 9 dimensions, returning copy-paste-ready fixes so AI agents can find and call it as a tool.", "version": "0.0", "x-build-id": "8781UfskNopHPzPYB" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/shelvick~apify-discoverability-audit/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-shelvick-apify-discoverability-audit", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/shelvick~apify-discoverability-audit/runs": { "post": { "operationId": "runs-sync-shelvick-apify-discoverability-audit", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/shelvick~apify-discoverability-audit/run-sync": { "post": { "operationId": "run-sync-shelvick-apify-discoverability-audit", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "actor" ], "properties": { "actor": { "title": "Actor to audit", "minLength": 3, "maxLength": 200, "type": "string", "description": "Apify Actor to audit. Accepts either 'username/name' (e.g. 'shelvick/smart-page-fetcher', 'apify/website-content-crawler') or a 17-char Actor ID. Required. The audit fetches the Actor's Store metadata, latest build (input schema + dataset schema + full README), and Store row (pricing model + agentic-payment whitelist flag), then runs the 9-dimension rubric — 8 deterministic checks plus a live probe of whether the Actor surfaces in agent-facing Actor search — and returns a punch list." }, "canonical_url": { "title": "Canonical documentation URL (optional)", "type": "string", "description": "Optional canonical-page URL for this Actor (e.g. 'https://www.scotthelvick.com/tools/smart-page-fetcher/'). If provided, the audit fetches it and checks for a Store back-link (Store ↔ canonical cross-link is the highest-leverage off-platform GEO signal). If absent AND the target is in the shelvick/ namespace, the audit auto-derives 'https://www.scotthelvick.com/tools//'. Leave blank for any Actor without a canonical page." }, "include_suggestions": { "title": "Include LLM-generated suggestion text", "type": "boolean", "description": "Whether to invoke the LLM to generate copy-paste-ready improvement text for failed and warned checks. Default true. Set false to skip the suggestion LLM call — the audit still runs all 9 dimensions and returns the punch list with evidence, just without prose suggestions. (The live retrieval probe has its own switch, include_retrieval_probe.)", "default": true }, "include_retrieval_probe": { "title": "Run the live agent-retrieval probe", "type": "boolean", "description": "Run the live agent_retrieval dimension: query the agent-facing Actor search for the Actor's name and intent keywords, plus an external MCP-registry presence check. Default true. It generates its probe keywords with one small LLM call by default (deterministic fallback when no model key is set), independent of include_suggestions. Set false to skip the probe and that call; the other 8 dimensions still run.", "default": true }, "retrieval_keywords": { "title": "Retrieval keywords (optional override)", "type": "array", "description": "Optional explicit list of agent-intent search phrases to probe (e.g. 'url to markdown', 'bypass cloudflare', 'fetch javascript page'). When provided, these replace both the LLM-generated and deterministic keyword sets and no keyword LLM call is made — use it to test the exact phrasings you want the Actor to rank for. Ignored when include_retrieval_probe is false.", "items": { "type": "string" } } } }, "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 } } } } } } } } } }