# Video & Image Intelligence Tool - Content Analysis (`ntriqpro/video-intelligence-mcp`) Actor Analyze video and image content with AI. Understand scenes, objects, and visual information. - **URL**: https://apify.com/ntriqpro/video-intelligence-mcp.md - **Developed by:** [daehwan kim](https://apify.com/ntriqpro) (community) - **Categories:** AI, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $50.00 / 1,000 tool calls 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 ## Video & Image Intelligence Analyzer A powerful AI-powered tool to analyze images and videos. Provides structured tagging, detailed descriptions, mood analysis, text detection, virality scoring, and video frame-by-frame analysis. Perfect for Claude Desktop, Cursor, and other AI applications that need visual content intelligence. ### Features - **Image Tagging**: Structured JSON output (category, tags, mood, text detection, virality score) - **Image Description**: Detailed natural language descriptions - **Video Analysis**: Frame-by-frame breakdown with comprehensive summary - **Image Comparison**: Side-by-side analysis of two images - **Batch Processing**: Tag multiple images in one call - **15 Languages**: English, Spanish, Portuguese, French, German, Arabic, Hindi, Vietnamese, Thai, Indonesian, Turkish, Russian, Korean, Japanese, Chinese - **Custom Prompts**: Override default analysis with your own instructions - **No Cloud API Costs**: Uses local AI model (Qwen2.5-VL 7B) — no per-image fees to third parties ### Pricing **$0.05 per tool call** (pay-per-event). You only pay for successful analyses. Failed analyses are not charged. ### Installation #### Claude Desktop Add to your `claude_desktop_config.json`: ```json { # Actor input Schema ## `tool` (type: `string`): Select which analysis tool to use ## `url` (type: `string`): URL of the image or video to analyze ## `url1` (type: `string`): URL of the first image for comparison (compare_images tool) ## `url2` (type: `string`): URL of the second image for comparison (compare_images tool) ## `urls` (type: `string`): JSON array of image URLs for batch tagging. Example: ["url1", "url2"] ## `prompt` (type: `string`): Override default analysis prompt with custom instructions. Example: 'Focus on people and emotions' ## `language` (type: `string`): Response language ## `maxFrames` (type: `integer`): Maximum frames to analyze for video ## `fpsSample` (type: `number`): Frames per second sampling rate ## `maxTokens` (type: `integer`): Maximum tokens in response (256-1024) ## Actor input object example ```json { "tool": "tag_image", "url": "https://example.com/image.jpg", "url1": "https://example.com/image1.jpg", "url2": "https://example.com/image2.jpg", "language": "en", "maxFrames": 20, "fpsSample": 1, "maxTokens": 256 } ```` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("ntriqpro/video-intelligence-mcp").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("ntriqpro/video-intelligence-mcp").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 ntriqpro/video-intelligence-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ntriqpro/video-intelligence-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Video & Image Intelligence Tool - Content Analysis", "description": "Analyze video and image content with AI. Understand scenes, objects, and visual information.", "version": "1.0", "x-build-id": "b91bJlO3VFFNNkTdH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ntriqpro~video-intelligence-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ntriqpro-video-intelligence-mcp", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ntriqpro~video-intelligence-mcp/runs": { "post": { "operationId": "runs-sync-ntriqpro-video-intelligence-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ntriqpro~video-intelligence-mcp/run-sync": { "post": { "operationId": "run-sync-ntriqpro-video-intelligence-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "tool" ], "properties": { "tool": { "title": "Analysis Tool", "enum": [ "tag_image", "describe_image", "analyze_video", "compare_images", "batch_tag" ], "type": "string", "description": "Select which analysis tool to use", "default": "tag_image" }, "url": { "title": "Image/Video URL", "type": "string", "description": "URL of the image or video to analyze" }, "url1": { "title": "First Image URL", "type": "string", "description": "URL of the first image for comparison (compare_images tool)" }, "url2": { "title": "Second Image URL", "type": "string", "description": "URL of the second image for comparison (compare_images tool)" }, "urls": { "title": "Image URLs (Array)", "type": "string", "description": "JSON array of image URLs for batch tagging. Example: [\"url1\", \"url2\"]" }, "prompt": { "title": "Custom Prompt (Optional)", "type": "string", "description": "Override default analysis prompt with custom instructions. Example: 'Focus on people and emotions'" }, "language": { "title": "Response Language", "enum": [ "en", "es", "pt", "fr", "de", "ar", "hi", "vi", "th", "id", "tr", "ru", "ko", "ja", "zh" ], "type": "string", "description": "Response language", "default": "en" }, "maxFrames": { "title": "Max Frames (Video)", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum frames to analyze for video", "default": 20 }, "fpsSample": { "title": "FPS Sampling (Video)", "minimum": 0.1, "maximum": 10, "type": "number", "description": "Frames per second sampling rate", "default": 1 }, "maxTokens": { "title": "Max Response Length", "minimum": 256, "maximum": 1024, "type": "integer", "description": "Maximum tokens in response (256-1024)", "default": 256 } } }, "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 } } } } } } } } } } ```