Venice AI Image Generator MCP Server

What is Venice AI Image Generator MCP Server?

It is an MCP server that integrates with Venice AI for image generation and implements an interactive approval/regeneration workflow. It provides a bridge between LLMs (like Claude) and Venice AI’s image generation capabilities, with automatic multi-view generation via Gemini and optional 3D model creation using CUDA Multi-View Stereo.

How to use Venice AI Image Generator MCP Server?

Install the FastMCP library, set up Venice AI API credentials, implement the described MCP tools, run the server, and connect it to an LLM host. The user provides a text prompt; the LLM calls the server tools to generate, display, and handle approval/regeneration.

Key features of Venice AI Image Generator MCP Server

• Image generation from text prompts via Venice AI
• Clickable thumbs up/down approval workflow overlaid on images
• Automatic regeneration with same parameters on thumbs down
• Multi-view generation using Google Gemini after approval
• Individual thumbs up/down for each of four generated views
• 3D model creation from approved 2D views via CUDA Multi-View Stereo
• Lists available Venice AI models

Use cases of Venice AI Image Generator MCP Server

• Generating images with iterative user feedback and refinement
• Creating consistent multi-view images of an object for 3D modeling
• Enabling LLM-driven image creation with human-in-the-loop approval
• Automated 3D model generation from a single text prompt

FAQ from Venice AI Image Generator MCP Server

What tools does this MCP server provide?

It provides generate_venice_image, approve_image, regenerate_image, and list_available_models.

How does the approval workflow work?

The server displays the generated image with thumbs up/down icons. Clicking thumbs up approves the image; thumbs down triggers regeneration with the same parameters.

What happens after a user approves an image?

The system automatically processes the approved image through Gemini to generate four consistent views (front, right, left, back), each with its own approval workflow. Once all views are approved, a 3D model can be generated via CUDA Multi-View Stereo.

What dependencies does the server require?

Venice AI API credentials, FastMCP library, Google Gemini API credentials, and a dedicated Linux server with CUDA Multi-View Stereo for 3D model generation.

Where are generated images stored during the workflow?

Images are stored in an in-memory cache on the server for tracking approval status. Persistent storage is not implemented yet.