MCP Server Replicate

A FastMCP server implementation based on the Replicate API, focusing on providing resource-based access to AI model inference, especially good at image generation functions.

Image and video processing Artificial intelligence chatbots #Image generation #AI inference #Real-time updates #API integration .Python

rating : 2.5 points

downloads : 40

update time : 2025-04-29

What is Replicate MCP Server?

Replicate MCP Server is a fast MCP server based on the Replicate API that allows users to access the inference capabilities of AI models through resource management, and is particularly good at generating high-quality images.

How to use Replicate MCP Server?

You can connect to your device in just a few steps and start generating images or managing existing generated content. You can input prompts in natural language and also view the generation progress.

Applicable scenarios

Suitable for scenarios that require high-quality image generation, model selection guidance, and real-time status monitoring.

Main features

Text-to-image generationGenerate high-quality images based on detailed text descriptions, supporting multiple styles and quality presets.

Real-time updates and subscriptionsTrack the generation progress in real-time and receive status update notifications.

Template-driven parameter configurationEasily adjust model parameters through predefined templates to simplify complex settings.

Model discovery and selectionProvide a comprehensive range of model options to help users select the most suitable model.

Webhook integrationSupport external notification systems for convenient automated workflows.

Quality and style presetsProvide multiple quality levels (such as draft, balanced, high-quality, and extreme) and style options.

Progress tracking and status monitoringMonitor the status of generation tasks in real-time to ensure the tasks are completed smoothly.

Secure API key managementSupport secure storage and use of Replicate API keys.

Advantages and limitations

Advantages

Support high-quality image generation.

Provide multiple styles and quality presets.

Real-time updates and status tracking.

Easy to integrate into existing workflows.

Limitations

May require a certain amount of computing resources.

Some advanced features may require specific API permissions.

The configuration process may be a bit complicated for beginners.

How to use

Install Replicate MCP Server

Install the server using the UV tool or directly through PyPI.

Set the Replicate API key

Set your API key in the environment variables or configuration file.

Connect to Claude Desktop

Configure Claude Desktop to use Replicate MCP Server.

Start generating images

Input prompts in natural language, such as 'Create a realistic mountain landscape sunset image'.

Usage examples

Generate a realistic mountain landscape sunset imageThe user inputs the prompt 'Create a realistic mountain landscape sunset image', and the server generates a high-quality mountain landscape image.

View recent generation recordsThe user inputs the prompt 'Show my recent image generation history', and the server returns a list of recently generated images.

Search for generation records in a specific categoryThe user inputs the prompt 'Find my generation records in the landscape category', and the server filters and returns the eligible images.

Frequently Asked Questions

How to install Replicate MCP Server?

How to set the Replicate API key?

What if the server does not appear in Claude Desktop?

What if an error occurs when generating images?

Related resources

Replicate MCP Server GitHub repository

Project source code and documentation.

Claude Desktop official documentation

Learn how to configure Claude Desktop.

Replicate API documentation

Gain in-depth understanding of Replicate API features.

🚀 MCP Server Replicate

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

🚀 Quick Start

This project offers a fast MCP server implementation for the Replicate API, enabling resource - based access to AI model inference, especially for image generation.

✨ Features

Fast Response: Optimized processing flow ensures low latency and high efficiency.
Multi - model Support: Compatible with various AI models to meet different needs.
Automated Management: Automatically handles resource allocation and task scheduling.
Scalable Architecture: Supports horizontal scaling to adapt to high - load scenarios.

📦 Installation

Install via PyPI:

pip install mcp-server-replicate

Clone the repository and install dependencies:

git clone https://github.com/gerred/mcp-server-replicate.git
cd mcp-server-replicate
uv pip install --system ".[dev]"

Start the server:

python -m mcp_server_replicate.start

💻 Usage Examples

Basic Usage

Generate Images

Natural Language Input:

Create a realistic image of a sunset over a snow - covered mountain, showing the snow - capped peaks.

Generate Based on Templates:

Create a picture of a mountain scenery using the landscape template.

Browse and Manage

View History:

Show all the images I recently generated.

Search for Specific Content:

Find all images related to "landscape".

Check Status:

How is the progress of the recent image generation?

📚 Documentation

🔧 Technical Details

Troubleshooting

Server Not Displayed in Claude Desktop

Check the log file:

tail -n 20 -f ~/Library/Logs/Claude/mcp*.log

Validate the configuration:

Ensure that the paths in claude_desktop_config.json are absolute paths.
Confirm that UV is installed and in the PATH environment variable.
Check if the Replicate API key is set.

Try restarting the Claude desktop:

restart claude-desktop

Development

Clone the repository:

git clone https://github.com/gerred/mcp-server-replicate.git
cd mcp-server-replicate

Install development dependencies:

uv pip install --system ".[dev]"

Install pre - commit hooks:

pre-commit install

Run tests:

pytest

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

🤝 Contribution

We welcome any contributions! Please refer to our Contribution Guide for more information.

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