Frequently Asked Questions about MCP Server Replicate

Q: What is MCP Server Replicate?

A: MCP Server Replicate is a FastMCP server implementation for the Replicate API, providing resource-based access to AI model inference with a focus on image generation.

Q: What are the key features of MCP Server Replicate?

A: Key features include resource-based image generation, real-time updates, template-driven parameter configuration, model discovery, webhook integration, quality presets, progress tracking, and secure API key management.

Q: What prompts are available in MCP Server Replicate?

A: The server provides prompts such as Text to Image (primary), Image to Image (coming soon), Model Selection, and Parameter Help.

Q: What are the prerequisites for installing MCP Server Replicate?

A: You need Python 3.11 or higher, a Replicate API key, and UV for dependency management.

Q: How do I install MCP Server Replicate?

A: You can install it via Smithery or manually using UV or pip.

Q: How do I integrate MCP Server Replicate with Claude Desktop?

A: You need to add the server configuration to your claude_desktop_config.json file and set your Replicate API key.

Q: How do I use MCP Server Replicate once connected to Claude Desktop?

A: You can generate images with natural language, browse your generations, search through generations, and check generation status.

Q: What do I do if the server is not showing up in Claude Desktop?

A: Check the Claude Desktop logs, verify your configuration, ensure UV is installed and in your PATH, and check that your Replicate API key is set. Try restarting Claude Desktop.

Q: Where can I find the documentation for MCP Server Replicate?

A: You can find the Implementation Plan, Contributing Guide, API Reference, Resource System documentation, and Template System documentation in the repository.

Q: How can I contribute to the MCP Server Replicate project?

A: Please see the Contributing Guide for details on how to contribute.

Q: What license is MCP Server Replicate released under?

A: This project is licensed under the MIT License.