Higgsfield MCP adds video generation to Cursor

// 2h agoPRODUCT LAUNCH

Higgsfield MCP adds video generation to Cursor

Higgsfield has launched a Model Context Protocol (MCP) server that connects its video generation engine directly to IDEs like Cursor and agents like Claude. The integration enables developers to generate cinematic videos, product photos, and marketing assets natively within their workflow using over 30 distinct AI models.

// ANALYSIS

Embedding generative AI video directly into coding environments via MCP is a significant evolution for autonomous developer loops. Developers can now prompt Cursor to write landing page code and concurrently generate the required hero video assets, collapsing the "build" and "market" phases. The server abstracts away 30+ disparate models (including Veo, Kling, and Seedance) into a unified toolset accessible by any MCP-compatible client. It signals a shift from pure code-retrieval MCP tools to creative execution, allowing agents to handle full-stack production. The integration removes the need to constantly context-switch into separate web applications to test or generate visual media.

// TAGS

higgsfieldcursormcpvideo-genimage-gendevtoolagent

DISCOVERED

2h ago

2026-05-22

PUBLISHED

2h ago

2026-05-22

RELEVANCE

8/ 10

AUTHOR

higgsfield

// KEEP READING

More AI developer news from the feed

EXPLORE FULL FEED

NEWS1h ago

Google’s WebMCP turns browser agents into tool callers

WebMCP is Google’s proposed web standard for exposing structured tools to AI agents through JavaScript and annotated HTML forms. It is meant to give browser agents a reliability layer so they can invoke declared actions instead of inferring intent from pixels, with a Chrome flag available for local development and an origin trial planned for Chrome 149.

MODEL1h ago

Gemini Omni adds conversational video generation, editing

Google’s Gemini Omni is a multimodal video model that can generate and edit video from text, images, audio, and video inputs. The big shift is conversational, step-by-step editing with stronger scene consistency and reference-based creation.

LAUNCH1h ago

LobeHub says coordination beats model quality

LobeHub is positioning itself as a Chief Agent Operator for multi-agent work: you give it a high-level goal, and it coordinates specialized agents, routes work across models, and surfaces only the decisions that need human input. The pitch is less “another chatbot” and more an orchestration layer for long-running, heterogeneous AI workflows, with open-source/self-hosted deployment and integration into the channels teams already use.