WavespeedMCP MCP Server

What does “WavespeedMCP” MCP Server do?

WavespeedMCP is a Model Control Protocol (MCP) server implementation designed for WaveSpeed AI services. It provides a standardized interface that enables AI assistants to access advanced image and video generation capabilities through the MCP protocol. With WavespeedMCP, developers can enhance their workflows by integrating tools for high-quality image creation (including text-to-image, image-to-image, and inpainting), dynamic video generation from images, and flexible resource handling (supporting URL, Base64, and local file outputs). The server also offers robust error handling, detailed logging, and multiple configuration options, making it a versatile component for connecting AI systems with external media generation services.

List of Prompts

No prompt templates are explicitly listed in the available documentation.

List of Resources

No explicit list of MCP “resources” found in the documentation.

List of Tools

The server defines and exposes several tools as described in its architecture and features:

• Text-to-Image Generation
  • Create high-quality images from text prompts, supporting options such as LoRA models and inpainting.
• Image-to-Image Generation
  • Transform or modify images based on input images and additional parameters.
• Video Generation
  • Convert static images into dynamic videos with customizable motion parameters.

Use Cases of this MCP Server

• Automated Image Generation
  • Developers can automate the creation of detailed, high-quality images from text prompts, streamlining design and prototyping tasks.
• Video Content Creation
  • Transform static images into engaging video content programmatically, supporting creative workflows or marketing automation.
• Flexible Output Handling
  • Output generated media in various formats (URL, Base64, local file), making integration with diverse platforms seamless.
• Enhanced AI Assistant Capabilities
  • Equip AI agents with sophisticated visual generation tools, allowing them to fulfill complex user requests involving image or video synthesis.
• Custom Workflow Integration
  • Integrate with IDEs or automation platforms (e.g., Claude Desktop) to trigger media generation as part of larger development pipelines.

How to set it up

Windsurf

1. Ensure Python 3.11+ is installed.
2. Obtain a WaveSpeed API key from WaveSpeed AI.
3. Locate your Windsurf MCP configuration file.
4. Add the WavespeedMCP server using the following JSON snippet:

   {
     "mcpServers": {
       "Wavespeed": {
         "command": "wavespeed-mcp",
         "env": {
           "WAVESPEED_API_KEY": "wavespeedkey"
         }
       }
     }
   }
   

5. Save the configuration and restart Windsurf.
6. Verify the server is connected.

Claude

1. Install Python 3.11+ and your WaveSpeed API key.
2. Generate the Claude Desktop config file:

   python -m wavespeed_mcp --api-key your_api_key_here --config-path /path/to/claude/config
   

3. Start the WavespeedMCP server:

   wavespeed-mcp --api-key your_api_key_here
   

4. Launch Claude Desktop to use the configured tools.

Cursor

1. Install Python 3.11+.
2. Obtain your WaveSpeed API key.
3. Edit Cursor’s MCP config:

   {
     "mcpServers": {
       "Wavespeed": {
         "command": "wavespeed-mcp",
         "env": {
           "WAVESPEED_API_KEY": "wavespeedkey"
         }
       }
     }
   }
   

4. Save and restart Cursor.

Cline

1. Ensure Python 3.11+ is present.
2. Acquire a WaveSpeed API key.
3. Add to Cline’s MCP configuration:

   {
     "mcpServers": {
       "Wavespeed": {
         "command": "wavespeed-mcp",
         "env": {
           "WAVESPEED_API_KEY": "wavespeedkey"
         }
       }
     }
   }
   

4. Save and restart Cline.

Securing API Keys
Store API keys as environment variables instead of hardcoding:

{
  "mcpServers": {
    "Wavespeed": {
      "command": "wavespeed-mcp",
      "env": {
        "WAVESPEED_API_KEY": "${WAVESPEED_API_KEY}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "Wavespeed": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “Wavespeed” to whatever the actual name of your MCP server is, and replace the URL with your own MCP server URL.

Overview

Based on the above, WavespeedMCP offers robust image/video generation tools and secure configuration, but lacks explicit prompt templates, resources, and sampling/roots support in the available docs. It is well-documented for setup and integration.

Our opinion

WavespeedMCP is a focused and practical MCP server for generative AI media workflows. Its documentation is clear on setup, security, and tool capabilities, but lacks detail on prompt templates and resource primitives. Sampling and roots support are not discussed. Overall, it is strong for its core purpose, but limited in MCP extensibility features.

Score: 6/10

MCP Score