Skip to main content
Glama

get_transcript

Extract text transcripts from YouTube videos to access video content in written form.

Instructions

Get the transcript of a YouTube video

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes

Implementation Reference

  • The main handler function for the 'get_transcript' MCP tool. It takes a YouTube URL, uses helper functions to process the video and fetch the transcript from the external API, handles errors, and returns the transcript text.
    @mcp_server.tool(name="get_transcript", description="Get the transcript of a YouTube video")
    async def get_transcript(url: str) -> str:
        """Get the transcript of a YouTube video.
    
        This tool processes a video and retrieves its transcript. It can efficiently
        handle YouTube URLs by extracting the video ID and checking if it's already
        been processed before submitting a new request.
    
        Args:
            url: The YouTube video URL
            
        Returns:
            The video transcript as text
        """
        logger.info(f"Getting transcript for URL: {url}")
        
        # Process the video to ensure it's ready
        success, video_id, error_message = await process_video(url)
        
        if not success:
            logger.error(f"Failed to process video: {error_message}")
            return f"Error: {error_message}"
        
        # Get the transcript from the API
        transcript_response = await make_yt_api_request(f"/api/videos/{video_id}/transcript")
        
        if not transcript_response:
            error_msg = "Failed to retrieve transcript."
            logger.error(error_msg)
            return f"Error: {error_msg}"
        
        # Check if the response is a string or a JSON object
        if isinstance(transcript_response, str):
            return transcript_response
        elif isinstance(transcript_response, dict) and "transcript" in transcript_response:
            return transcript_response["transcript"]["text"]
        else:
            error_msg = "Unexpected response format from API."
            logger.error(error_msg)
            return f"Error: {error_msg}"
  • Critical helper function called by get_transcript to handle video processing: extracts YouTube ID, checks existing status, submits if needed, and polls until completed or error.
    async def process_video(url: str) -> tuple[bool, str, str]:
        """Helper function to submit a video for processing and wait for completion.
        
        This function now tries to optimize API calls by:
        1. Extracting YouTube ID from URL when possible
        2. Checking if video is already processed using YouTube ID directly
        3. Only submitting for processing if needed
        
        Args:
            url: The YouTube video URL
            
        Returns:
            A tuple of (success, video_id, error_message)
        """
        try:
            # Step 1: Try to extract YouTube ID from URL
            youtube_id = extract_youtube_id(url)
            video_id = ""
            
            if youtube_id:
                logger.info(f"Extracted YouTube ID: {youtube_id} from URL: {url}")
                
                # Step 2: Check if video has already been processed using YouTube ID directly
                status_response = await make_yt_api_request(f"/api/videos/{youtube_id}")
                
                if status_response and "status" in status_response:
                    video_id = youtube_id
                    logger.info(f"Found existing video with YouTube ID: {youtube_id}, status: {status_response.get('status')}")
                    
                    # If video is already processed or processing, we can use this ID
                    if status_response.get("status") == "completed":
                        logger.info(f"Video already processed, using YouTube ID: {youtube_id}")
                        return True, youtube_id, ""
                    elif status_response.get("status") == "processing":
                        # Need to wait for processing to complete
                        logger.info(f"Video already processing, waiting for completion: {youtube_id}")
                        # Continue to polling step below with the YouTube ID
                        video_id = youtube_id
                    elif status_response.get("status") == "error":
                        error_message = status_response.get("message", "Unknown error occurred")
                        logger.error(f"Error with video: {error_message}")
                        return False, youtube_id, f"Error processing video: {error_message}"
            
            # Step 3: Submit video for processing if needed (if we don't have a video_id yet)
            if not video_id:
                logger.info(f"Submitting video for processing: {url}")
                
                submit_response = await make_yt_api_request("/api/videos", method="POST", json_data={"url": url})
                
                if not submit_response or "id" not in submit_response:
                    logger.error("Failed to submit video for processing")
                    return False, "", "Failed to submit video for processing."
                
                video_id = submit_response["id"]
                logger.info(f"Video submitted, received ID: {video_id}")
                await asyncio.sleep(1) # wait for 1 second before polling
            
            # Step 4: Poll for video processing status until it's complete
            max_attempts = 10
            attempts = 0
            
            while attempts < max_attempts:
                logger.info(f"Checking video status, attempt {attempts+1}/{max_attempts}")
                
                status_response = await make_yt_api_request(f"/api/videos/{video_id}")
                
                if not status_response:
                    logger.error("Failed to retrieve video status")
                    return False, video_id, "Failed to retrieve video status."
                
                status = status_response.get("status")
                logger.info(f"Video status: {status}")
                    
                if status == "completed":
                    logger.info(f"Video processing completed for ID: {video_id}")
                    return True, video_id, ""
                    
                if status == "error":
                    error_message = status_response.get("message", "Unknown error occurred")
                    logger.error(f"Error processing video: {error_message}")
                    return False, video_id, f"Error processing video: {error_message}"
                
                # Calculate backoff delay
                delay = await calculate_backoff_delay(attempts)
                logger.info(f"Waiting {delay:.1f}s before checking video status again, attempt {attempts+1}/{max_attempts}")
                
                await asyncio.sleep(delay)
                attempts += 1
            
            logger.error("Video processing timeout - too many attempts")
            return False, video_id, "Video processing timed out. Please try again later."
            
        except Exception as e:
            logger.error(f"Exception during video processing: {str(e)}")
            return False, "", f"An error occurred: {str(e)}"
  • Helper function used by get_transcript and process_video to make HTTP requests to the YouTube Translate API, handling GET/POST, errors, and special cases like subtitles.
    async def make_yt_api_request(endpoint: str, method: str = "GET", params: dict = None, json_data: dict = None) -> dict[str, Any] | str | None:
        """Make a request to the YouTube Translate API with proper error handling."""
        headers = {
            "X-API-Key": YOUTUBE_TRANSLATE_API_KEY,
            "Content-Type": "application/json"
        }
        
        url = f"{YT_TRANSLATE_API_BASE}{endpoint}"
        
        logger.info(f"Making API request: {method} {url}")
        if params:
            logger.info(f"Request params: {params}")
        if json_data:
            logger.info(f"Request data: {json_data}")
        
        async with httpx.AsyncClient() as client:
            try:
                if method.upper() == "GET":
                    response = await client.get(url, headers=headers, params=params, timeout=30.0)
                elif method.upper() == "POST":
                    response = await client.post(url, headers=headers, params=params, json=json_data, timeout=30.0)
                else:
                    logger.error(f"ERROR: Invalid HTTP method: {method}")
                    return None
                    
                response.raise_for_status()
                
                logger.info(f"API response status: {response.status_code}")
                
                # If the endpoint is for subtitles, directly return the text content
                if "/subtitles" in endpoint:
                    return response.text
                
                # For all other endpoints, return the JSON response
                return response.json()
            except Exception as e:
                logger.error(f"API request error: {str(e)}")
                return None
  • Decorator that registers the get_transcript function as an MCP tool with the specified name and description.
    @mcp_server.tool(name="get_transcript", description="Get the transcript of a YouTube video")
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but doesn't cover critical aspects like authentication needs, rate limits, error handling, or what the transcript output format looks like. This is a significant gap for a tool that likely involves external API calls.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to understand at a glance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of fetching transcripts from YouTube (likely involving external APIs), no annotations, and no output schema, the description is incomplete. It doesn't address behavioral traits, output format, or usage context, making it inadequate for an AI agent to use this tool effectively without additional information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description implies a 'url' parameter is needed but doesn't add meaning beyond what the input schema provides. With 0% schema description coverage, the description partially compensates by hinting at the parameter's purpose, but it doesn't specify URL format requirements or constraints, leaving the schema to handle all details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and the resource 'transcript of a YouTube video', making the purpose specific and understandable. However, it doesn't distinguish this tool from sibling tools like 'get_subtitles' or 'get_summary', which might offer similar functionality, so it lacks explicit differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios for preferring this over 'get_subtitles' or 'get_summary', nor does it specify prerequisites or exclusions, leaving usage context implied at best.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/brianshin22/youtube-translate-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server