Pureframe AI API Documentation

Pureframe AI exposes your video library as an MCP server. Once connected, any MCP-compatible AI client can search footage, list collections, retrieve video details, and index new footage — all through natural language. Try asking your agent: “Find the moment our CEO mentions pricing.”

What is MCP?

The Model Context Protocol (MCP) is an open standard for connecting AI clients to external tools and data sources. Instead of writing custom integration code for each AI product, you configure one MCP server and every MCP-compatible client — Claude, Cursor, VS Code, Codex, OpenCode, and others — can call it the same way.

How it works

Your AI client sends a natural-language request to the model (e.g. “find the moment someone mentions churn”)
The model decides to call the search_videos tool and generates its input
The MCP server forwards the call to Pureframe AI’s API and returns matching clips, including thumbnail_base64
The model reads the frame directly, in the same turn, without fetching a URL, and can answer follow-up questions about what’s visible

Available tools

Tool	Description
`search_videos`	Find moments matching a text query. Returns clips with timestamps, presigned URLs, and base64-encoded frame images
`list_collections`	List all collections in the library
`get_collection`	Get details about a specific collection: video count, total duration, storage used
`get_video`	Get metadata and a presigned playback URL for a specific video
`upload_video`	Upload a video file from the local filesystem into a collection for indexing. Returns a `job_id`. Local server only — not available on the remote MCP endpoint, which can’t read your files. Requires a key with write permission
`get_job_status`	Check an indexing job’s status and progress. Poll after `upload_video` until `done`, then the video is searchable

Next steps

Connect an Agent

Per-client setup, config files, and troubleshooting.

Agent Security

Scoping what an agent can do with your library.