Vimeo MCP Server

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What is the Vimeo MCP Server?

It's a bridge between Vimeo's video hosting platform and AI/developer tools via the Model Context Protocol (MCP), enabling natural language access to video management, automated content distribution, analytics tracking, and seamless workflow integration for video creators and content teams.

What are the main use cases for video teams?

Automated video uploads and metadata management, bulk video operations across your library, natural language analytics queries, scheduled content publishing, thumbnail customization at scale, privacy and access control automation, and cross-platform video distribution workflows.

How do I secure my Vimeo API credentials?

Store your Vimeo access token in environment variables using the 'VIMEO_ACCESS_TOKEN' variable in your MCP server configuration. Never commit credentials to version control or hardcode them in configuration files.

Can I use this for video analytics and performance tracking?

Yes, the Vimeo MCP Server provides access to comprehensive video analytics including view counts, engagement metrics, audience demographics, watch time data, and performance trends, enabling AI-powered insights and automated reporting.

Does this work with Vimeo's privacy controls?

Absolutely. The server supports full control over video privacy settings including public, private, password-protected, and domain-restricted access, allowing you to automate privacy management based on your content distribution strategy.

How do I use this MCP server inside FlowHunt?

Add the MCP component to your FlowHunt flow, open its configuration, and insert the Vimeo MCP server details in JSON format with your access token. Once configured, your AI agent will have access to Vimeo's video management and analytics capabilities for intelligent video workflow automation.