How Do I Let an AI Agent Look Through My Own Video Library?

How do I let an AI agent look through my own video library?

Index the videos once, then give the agent a search tool. Indexing turns each video into short, timestamped segments with a transcript, the text shown on screen and a description of what is visible. The search tool lets the agent query that index in plain language and get back the matching moments, with the file and the second each one starts. The usual way to hand an agent a tool today is an MCP server, which Claude Code, Cursor, Codex, Gemini CLI and Claude Desktop can all connect to.

Why can't I just give the agent my video files?

Because a library is far larger than anything a model can read at once. A chat model can watch one short video you upload, but an hour of footage is thousands of frames plus a transcript, and a library is hundreds or thousands of hours. The agent cannot hold that in its context, and re-watching everything on every question would be slow and expensive. An index does the watching once, ahead of time, so each question costs a search.

What are the two pieces I need?

Piece

What it does

Examples

An index of the videos	Splits each video into segments and stores what was said, what was shown and what is visible, with timestamps	A video understanding pipeline over your storage bucket
A tool the agent can call	Lets the agent search the index and read the results	An MCP server, or a function-calling tool in your own agent

With both in place, a question such as "find every clip where a customer complains about delivery" becomes a search call, and the agent can follow up with narrower searches, compare the results or summarize them.

How do I keep the videos private?

Keep the videos in your own storage and index them where they live, so nothing has to be copied into a consumer app. The agent sees only search results, and an API key scoped to one project limits what it can reach. If nothing may leave your machines at all, run the transcription and vision models locally and expose the index through a local MCP server.

How do I set this up with Mixpeek?

Mixpeek indexes video where it already lives, in S3, GCS or any bucket, into timestamped segments with transcripts, on-screen text, descriptions and visual embeddings. The Footage Intelligence template sets up that pipeline in one click.

To connect your agent, run:

curl -fsSL https://mixpeek.com/install | sh

It adds the hosted Mixpeek MCP server to Claude Code, Codex, Gemini CLI and Cursor, whichever you have, using your API key. The agent then has tools such as list_namespaces, search_namespace and execute_retriever. Open mixpeek.com/install in a browser to read the script before you run it.

Related: how to find one specific moment in hours of footage, MCP tools for multimodal AI agents and what it costs to make a video library searchable.

Frequently Asked Questions

How do I let an AI agent look through my own video library?

Index the videos into timestamped segments (transcript, on-screen text, a description and visual embeddings), then give the agent a search tool for that index, usually an MCP server. The agent searches in plain language and gets back the matching moments with their timestamps.

Can ChatGPT or Claude watch my whole video library?

They can read a short video you upload in a chat. Hours of footage do not fit in a model's context, so index the library and give the assistant a search tool; it then reads only the moments that match each question.

What is an MCP server?

A standard way to give an AI assistant tools. The assistant connects to the server and can then call the tools it lists, such as searching an index, the same way it would use a built-in feature.

Does the agent need access to my storage bucket?

No. The indexer reads the bucket; the agent only calls the search tool and receives results. Scope the agent's API key to the project it should search.