TikTok, Reels & Shorts Video Analyzer: hooks, scenes, text
Pricing
Pay per event
Every scraper hands you the metadata. This one watches the video and tells you why it worked.
You can access the TikTok, Reels & Shorts Video Analyzer: hooks, scenes, text programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in API & Integrations in Apify Console.
{ "openapi": "3.0.1", "info": { "version": "0.7", "x-build-id": "MQSHq0k54936ewoD4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/frameprobe~reel-teardown/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-frameprobe-reel-teardown", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/frameprobe~reel-teardown/runs": { "post": { "operationId": "runs-sync-frameprobe-reel-teardown", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/frameprobe~reel-teardown/run-sync": { "post": { "operationId": "run-sync-frameprobe-reel-teardown", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "videoUrls": { "title": "Direct video URLs", "type": "array", "description": "Direct media file URLs (ending in .mp4 and similar), analyzed straight away. Social page links (tiktok.com, instagram.com, youtube.com) will NOT work: those platforms block video fetching from cloud servers, so chain a scraper and use the dataset field above. If you paste a page link anyway, the Actor tells you which scraper to run.", "items": { "type": "string" } }, "datasetId": { "title": "Dataset from a scraper run (recommended)", "type": "string", "description": "The recommended input. Run a TikTok, Instagram or YouTube scraper first, then pick its dataset here: this Actor reads the media URLs the scraper already collected and analyzes each one. Recognised fields include videoUrl, videoUrlNoWaterMark, mediaUrl, webVideoUrl and url." }, "maxVideos": { "title": "Maximum videos", "minimum": 1, "maximum": 100, "type": "integer", "description": "Hard stop on how many videos this run analyzes, so a large input list cannot spend more than you expect. At $0.04 per analyzed video, 100 videos is a $4.00 ceiling.", "default": 10 }, "framesPerVideo": { "title": "Frames per video", "minimum": 2, "maximum": 12, "type": "integer", "description": "How many stills are sampled and sent to the vision model. More frames means a more accurate read of structure and on-screen text, and a higher bill from your model provider. Frames are always dense over the opening seconds and spread across the rest.", "default": 6 }, "includeOnScreenText": { "title": "Extract on-screen text", "type": "boolean", "description": "Transcribe the text visible in the frames into the onScreenText field. Turning this off trims what the model writes back, which slightly lowers your model bill. It does not change how many frames are read.", "default": true }, "language": { "title": "Output language", "type": "string", "description": "The language for the written fields (hookNotes, whyItWorked, visualSystem, and so on). Classification values such as hookStyle stay in English so they can be filtered and counted.", "default": "en" }, "visionApiKey": { "title": "Your vision model API key", "type": "string", "description": "Your own Anthropic API key, from console.anthropic.com/settings/keys. Analysis runs on your account and you pay Anthropic directly, at their rates. The key is used for the run and is never stored or logged. Leave this empty for a free preview: the videos are still downloaded and measured (duration, resolution, scene count, cut cadence) and you are charged nothing per video." }, "visionProvider": { "title": "Provider", "enum": [ "anthropic" ], "type": "string", "description": "Which API your key belongs to. v1 supports Anthropic only; more providers are planned.", "default": "anthropic" }, "visionModel": { "title": "Model (optional)", "type": "string", "description": "Leave blank for claude-sonnet-5, the balanced default. Set claude-opus-5 for the most capable read, or claude-haiku-4-5 to lower your own inference bill. The id is checked against your account before the run starts, and an invalid one fails immediately with the list of models you can use." } } }, "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 } } } } } } } } }}OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.
OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.
By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.
You can download the OpenAPI definitions for TikTok, Reels & Shorts Video Analyzer: hooks, scenes, text from the options below:
If you’d like to learn more about how OpenAPI powers GPTs, read our blog post.
You can also check out our other API clients: