Twelve Labs MCP. Search videos with natural language queries.
Twelve Labs (Video Understanding) provides your agent with multimodal AI capabilities to index video content and extract deep semantic insights. You can query vast video libraries using natural language, locating specific objects, actions, or speakers across hours of footage without manual tagging. This MCP handles the entire process: asset upload, indexing, embedding generation, and complex search retrieval.
Give Claude and any AI agent real-world access
Search through indexed video content using natural language queries to pinpoint exact time stamps for objects or actions.
Handle large-scale asset uploads, confirming multipart sessions and monitoring the indexing status of videos from URLs or local files.
Create multimodal vector embeddings asynchronously or synchronously to power advanced machine learning workflows on video assets.
Define and populate collections of entities, such as people or objects, allowing your agent to track specific subjects across multiple videos.
Execute deep analysis tasks on video assets to extract structured data points from both visual frames and audio tracks.
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What AI agents can do with Twelve Labs (Video Understanding): 18 Tools
These tools give your agent granular control over every stage of multimodal AI workflow, from uploading assets to generating detailed search indexes.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Twelve Labs (Video Understanding) MCPAnalyze Async
Starts a background job to break down and analyze video content into segments.
Create Multipart Upload
Starts a segmented upload session, allowing you to reliably transfer very large...
Get Index
Retrieves the full details of a specific index using its unique ID number.
Analyze Sync
Analyzes and breaks down video content instantly, returning results immediately.
Confirm Multipart Upload
Verifies the details of a large, segmented file upload session before starting the...
Create Asset
Uploads raw video content to begin the process of creating an indexed digital asset.
Update Index
Changes the descriptive name of an existing video index without affecting its underlying data.
Create Entity Collection
Sets up a group or collection designed to hold and categorize specific types of...
Create Entity
Adds a single entity, such as a person's name, into an existing defined collection.
Create Index
Initializes and names a new index that will store all the video metadata for later...
Delete Index
Removes an entire index from the system when the associated project or data is no...
Embed Async
Generates vector embeddings for video content in the background, preparing it for semantic search.
Embed Sync
Creates vector embeddings for video content instantly, useful for small-scale testing and immediate use cases.
Get Indexed Asset
Fetches all the structured data and metadata associated with an already uploaded and...
Index Asset
Sends a specific asset to be processed and added to a pre-existing index.
List Indexes
Retrieves a list of all available indexes, showing their names and IDs.
Report Multipart Progress
Checks and reports the current progress status of a large, ongoing multipart upload...
Search
Runs a natural language query against an index to find specific moments or time segments within videos.
Security and governance baked right in.
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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Make Your AI Do More
Start with Twelve Labs (Video Understanding), then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
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Finding a single moment in hours of footage is painful work.
Today, if you're reviewing an incident or curating content for marketing, the process is brutal. You open your video management system, and instead of getting a direct answer, you are faced with endless thumbnails and time sliders. You have to click play, scrub through minutes of irrelevant footage, pause it, take notes, and copy down timestamps manually. It's an exercise in exhaustion.
With this MCP, your agent handles the slog. Instead of manual clicking, you just ask a natural language question like, 'Find every instance of the blue widget being handled.' The system processes that request against your indexed videos and spits out exactly what you need—the time codes, the relevant clips, and sometimes even structured data about the object itself.
Twelve Labs (Video Understanding) MCP provides search access to visual context.
You eliminate the manual steps of tagging. You don't have to wait for a human editor or ML engineer to go through footage and label every object or action point; you simply establish an index using `create_index`, and your agent does the deep work automatically.
The difference is radical: instead of viewing video archives as passive recordings, they become active, queryable knowledge bases. Your AI client can now interact with them like a highly specialized research assistant.
What Twelve Labs MCP does for your AI
This MCP gives your agent the ability to 'watch' videos and understand what it sees and hears. Instead of manually reviewing massive video archives, you can now query them using plain language—asking things like, "Show me every time someone mentions Q3 revenue" or "Find all shots featuring a red car." When you connect this MCP via Vinkius, your agent gets direct access to the tools needed to index videos and run deep analyses.
It handles everything from uploading assets to creating searchable indexes. Your agent can process visual and audio data simultaneously, generating structured insights about entities and moments in time. You simply tell your AI client what it needs, and this MCP does the heavy lifting of turning raw video files into actionable, machine-readable data points.
019e38ff-bc3e-73d5-bb9b-0eb183ea0793 How to set up Twelve Labs MCP
The bottom line is that your videos transform from static files into a fully searchable, structured knowledge base accessible through simple conversation.
First, you create a new index using the create_index tool. This establishes the container where your searchable video metadata will live.
Next, you upload your videos, either by confirming a multipart session or calling index_asset. The system then processes and embeds the content, making it ready for search.
Finally, you query the index using natural language prompts via the search tool. Your agent returns specific time stamps, objects, or actions found within the indexed footage.
Who uses Twelve Labs MCP
This MCP is for technical teams drowning in video data. It's the media manager who spends hours manually tagging footage, or the security analyst who needs to find a specific event that happened months ago—you need this to cut through the noise and get direct answers.
Uses this MCP to organize massive video libraries by creating indexes and running deep analysis tasks, ensuring all b-roll or source footage is cataloged and searchable.
Queries hours of surveillance or body camera footage using natural language to find specific objects (like a red vehicle) or actions without watching every second.
Integrates video understanding into complex agent workflows, utilizing tools like embed_async and create_entity_collection to build multimodal pipelines.
Benefits of connecting Twelve Labs MCP
Twelve Labs MCP use cases
Finding evidence in old security footage
A security analyst needs to know when a specific employee entered the restricted area. Instead of watching days of footage, they ask their agent: 'Search index idx_security for any instance of Employee X near Door Y.' The MCP uses search and returns precise time stamps.
Curating marketing b-roll quickly
A content creator needs quick shots of a product in use. They upload 50 videos, create an index using create_index, and then ask their agent to find 'close up shots of the packaging' across all assets.
Building knowledge retrieval for legal cases
A developer needs to build a system that answers questions based on video testimony. They use embed_async and populate an index, allowing their agent to retrieve visual context using semantic search via the get_indexed_asset tool.
Analyzing product failure points
An operations team needs to find where a machine fails. They run synchronous analysis (analyze_sync) on video feeds, which extracts structured data about component failure rates and automatically logs them into an entity collection.
Twelve Labs MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Treating videos like PDFs
Trying to search for a keyword ('meeting') using only text transcription results fails when the conversation is interrupted by visual cues or background noise.
You must use this MCP's native multimodal capabilities. Use the search tool against an indexed asset; it finds 'meeting' even if it's implied visually, not just spoken.
Over-relying on basic indexing
Just uploading a video and expecting search results for specific people or objects that aren't labeled manually.
Always use the dedicated tools. First, establish your data with create_index, then populate it by running deep analysis tasks or using embed_async to generate proper embeddings.
Assuming single-shot processing
Trying to process a 10GB video file in one go, which often leads to timeouts and failed transfers.
Use the robust upload mechanisms. Start with create_multipart_upload to manage large files reliably, then call report_multipart_progress to confirm successful transfer.
When to use Twelve Labs MCP
Use this MCP if your primary challenge is finding information inside video content—if the data you need exists visually or audibly within a stream of footage. You should use it when simple text search, like searching PDFs or databases, won't cut it because context matters. Don't use this if all you need to do is store metadata about videos; for that, basic cloud storage tools suffice. If you only need to transcribe speech, other dedicated transcription services might work better. However, if you need the AI to understand what the people in the video are doing, or who they are interacting with, this MCP gives you the full multimodal tooling set via create_entity and search that no simple file storage system can match.
Frequently asked questions about Twelve Labs MCP
How do I start using the Twelve Labs (Video Understanding) MCP? +
You subscribe to this MCP on Vinkius and provide your API key. Then, you use the create_index tool first to establish a searchable container for your data.
Can I search my videos using natural language with Twelve Labs (Video Understanding) MCP? +
Yes, that's its main purpose. The search tool lets you type what you are looking for—like 'a person arguing about contracts'—and it returns time codes across your indexed content.
Is Twelve Labs (Video Understanding) MCP better than just uploading videos to Google Drive? +
Absolutely. Standard storage services only hold the file; this MCP actually analyzes the contents, creating structured indexes and allowing semantic search based on objects or actions.
What is the difference between `analyze_sync` and `analyze_async` with Twelve Labs (Video Understanding) MCP? +
analyze_sync gives you immediate results for small tasks, but analyze_async handles large videos or complex jobs in the background without timing out your agent session.
How do I upload a massive video file to Twelve Labs (Video Understanding) MCP? +
You use the multipart tools. First, call create_multipart_upload to start the session, then send chunks of data and monitor progress with report_multipart_progress.
How do I list all my existing video indexes? +
You can use the list_indexes tool. It will return a list of all indexes available in your Twelve Labs account, including their IDs and configuration.
Can I search for a specific moment inside my videos using text? +
Yes! Use the search tool by providing an index_id and a search query. The AI will find the most relevant timestamps and video segments based on your description.
How do I add a new video to an index for analysis? +
First, use create_asset with a public URL to upload the video. Then, use the index_asset tool with the resulting asset_id and your target index_id to start the processing.