# YouTube Transcript MCP Server (`nyxar_dev/youtube-transcript-mcp`) Actor Extract transcripts, timestamps, and searchable text from any YouTube video for AI agents. Supports multiple languages, keyword search within transcripts, and timestamped output. Built on Model Context Protocol (MCP) for seamless integration with Claude, GPT, and other AI tools. - **URL**: https://apify.com/nyxar\_dev/youtube-transcript-mcp.md - **Developed by:** [Nyxar Dev](https://apify.com/nyxar_dev) (community) - **Categories:** AI, MCP servers, Developer tools - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## YouTube Transcript MCP Server Extract **transcripts, timestamps, and searchable text** from any YouTube video via MCP. This server connects to YouTube's caption system and returns clean, structured transcript data for any compatible client. ### What does YouTube Transcript MCP Server do? This MCP server connects to YouTube's caption system, enabling you to: - **Extract full transcripts** from any YouTube video with captions enabled - **Get timestamped transcripts** for precise moment-by-moment reference - **Search within transcripts** to find exactly when specific topics are discussed Running on the Apify platform gives you automatic scaling, zero infrastructure management, and pay-per-use pricing. ### Why use YouTube Transcript MCP Server? - **Research & summarization**: Process hours of video content in seconds - **Content repurposing**: Extract video content for blog posts, newsletters, or social media - **Meeting notes**: Pull transcripts from recorded meetings and presentations - **Education**: Search lecture videos for specific topics or concepts - **Competitive analysis**: Analyze competitor YouTube content at scale - **Multilingual support**: Fetch transcripts in any available language ### How to use YouTube Transcript MCP Server 1. Get your Apify API token from [Apify Console](https://console.apify.com/settings/integrations) 2. Add this MCP server to your client's configuration 3. Start fetching YouTube transcripts #### MCP client configuration Add to your MCP client config (e.g. `claude_desktop_config.json`): ```json { "mcpServers": { "youtube-transcript": { "type": "url", "url": "https://YOUR_USERNAME--youtube-transcript-mcp.apify.actor/mcp", "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" } } } } ```` ### Tools #### `get_transcript` Returns the full plain-text transcript of a YouTube video. Ideal for summarization, analysis, and content extraction. **Input:** - `videoUrl` (required) — YouTube URL or video ID - `lang` (optional) — Language code (e.g. "en", "es", "fr") #### `get_transcript_with_timestamps` Returns the transcript with `[MM:SS]` timestamps on each line. Great for referencing specific moments in a video. **Input:** - `videoUrl` (required) — YouTube URL or video ID - `lang` (optional) — Language code #### `search_transcript` Searches the transcript for a keyword or phrase and returns only the matching segments with timestamps. **Input:** - `videoUrl` (required) — YouTube URL or video ID - `query` (required) — Search term or phrase - `lang` (optional) — Language code ### Output example ```json { "content": [ { "type": "text", "text": "[0:18] We're no strangers to love\n[0:22] You know the rules and so do I\n[0:27] A full commitment's what I'm thinking of" } ] } ``` ### Pricing Each transcript fetch costs **$0.03**. There are no monthly fees or minimum charges — you only pay when a tool is called. | Usage | Estimated cost | |-------|---------------| | 10 transcripts/day | ~$9/month | | 100 transcripts/day | ~$90/month | | 1,000 transcripts/day | ~$900/month | ### Supported URL formats - `https://www.youtube.com/watch?v=VIDEO_ID` - `https://youtu.be/VIDEO_ID` - `https://youtube.com/embed/VIDEO_ID` - Raw video ID (11 characters) ### Limitations - Only works with videos that have captions/subtitles enabled - Auto-generated captions may contain errors - YouTube may rate-limit requests from a single IP under heavy use ### FAQ **Is this legal?** This server accesses publicly available YouTube caption data. It does not download or redistribute video content. **What if a video has no transcript?** The tool returns a clear error message indicating no transcripts are available. **Can I get auto-generated captions?** Yes — if a video only has auto-generated captions, those are returned by default. # Actor input Schema ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("nyxar_dev/youtube-transcript-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("nyxar_dev/youtube-transcript-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 nyxar_dev/youtube-transcript-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nyxar_dev/youtube-transcript-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "YouTube Transcript MCP Server", "description": "Extract transcripts, timestamps, and searchable text from any YouTube video for AI agents. Supports multiple languages, keyword search within transcripts, and timestamped output. Built on Model Context\n Protocol (MCP) for seamless integration with Claude, GPT, and other AI tools.", "version": "1.0", "x-build-id": "4T9h18glad0GfhBZU" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nyxar_dev~youtube-transcript-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nyxar_dev-youtube-transcript-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nyxar_dev~youtube-transcript-mcp/runs": { "post": { "operationId": "runs-sync-nyxar_dev-youtube-transcript-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nyxar_dev~youtube-transcript-mcp/run-sync": { "post": { "operationId": "run-sync-nyxar_dev-youtube-transcript-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": {} }, "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 } } } } } } } } } } ```