Fast Video Cataloger: MCP server, setup and security

What Fast Video Cataloger does

According to its repository: “Search, view and tag a local video library cataloged by Fast Video Cataloger: transcripts, scene thumbnails, visual search, keywords and bins.”

The Model Context Protocol (MCP) is a standard plug between an AI agent and an outside service: once the server is connected, its tools show up in the agent, which decides when to call them. Fast Video Cataloger is listed under Image, video and audio, a category of 67 servers in the directory, where it ranks #65 by stars.

Installing and connecting Fast Video Cataloger

Distribution modes declared in the official MCP registry:

• MCPB bundle (one-click install in Claude Desktop)

It works with any MCP-compatible client (Claude Desktop, Claude Code, Cursor, VS Code, Codex and most recent agents). You add one entry to the client's MCP configuration: server name, start command or URL, environment variables. Project language: C#. Listed systems: Windows.

The registry lists one secret variable to provide (API key, access token). Keep them in the client's environment variables, never in a shared file or a repository.

Security: what to check

Connecting an MCP server means installing software that talks straight to your agent: it runs with your rights and the model trusts its tool descriptions. The risk therefore depends on what it can touch and how far its code can be trusted.

Points found for Fast Video Cataloger:

• Secrets: the server acts with your credentials. Create a dedicated key with minimum rights and an expiry date.

Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.

Project activity and maintenance

Created on October 7, 2026, the frelo/fvc-mcp repository has 0 stars and 0 forks, with 0 open issues. It has more stars than 4% of the servers in the directory. It received changes this month. Version published in the registry: 10.4.0 (October 8, 2026).

Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.