WavespeedMCP MCP Server

@WaveSpeedAI

リポジトリ

基本情報

カテゴリ

その他

トランスポート

stdio

作者

WaveSpeedAI

WavespeedMCP MCP Server について

概要はまだありません

#mcp-server #image-generation #video-generation

WavespeedMCP MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-server-wavespeedai": {
      "command": "python",
      "args": [
        "-m",
        "wavespeed_mcp",
        "--api-key",
        "your_api_key_here",
        "--config-path",
        "/path/to/claude/config"
      ]
    }
  }
}

WavespeedMCP MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is WavespeedMCP?

WavespeedMCP is a Model Control Protocol (MCP) server implementation for WaveSpeed AI services. It provides a standardized interface for accessing WaveSpeed’s image and video generation capabilities through the MCP protocol.

How to use WavespeedMCP?

Install via pip install wavespeed-mcp, set the WAVESPEED_API_KEY environment variable, and start the server with wavespeed-mcp --api-key your_api_key_here. Integrate with Claude Desktop by generating a configuration file using python -m wavespeed_mcp --api-key your_api_key_here --config-path /path/to/claude/config.

Key features of WavespeedMCP

Advanced image generation with text-to-image, image-to-image, inpainting, and LoRA support
Dynamic video generation from static images with customizable motion parameters
Optimized API polling with intelligent retry and progress tracking
Flexible resource output modes: URL, Base64, and local files
Comprehensive error handling with specialized exception hierarchy
Robust logging with rotating file handler and configurable levels

Use cases of WavespeedMCP

Generate high-quality images from text prompts for creative projects
Transform static images into videos with adjustable motion settings
Enhance existing images using inpainting or image-to-image workflows
Automate media generation in applications via MCP (e.g., Claude Desktop)

よくある質問

What does WavespeedMCP provide beyond the raw WaveSpeed API?

WavespeedMCP wraps the WaveSpeed API into the MCP standard, adding intelligent polling, progress tracking, retry logic, and multiple output modes (URL, Base64, local). It also integrates seamlessly with MCP-compatible clients like Claude Desktop.

What are the runtime requirements for WavespeedMCP?

Python 3.11 or higher is required. You must also have a valid WaveSpeed API key, obtainable from the WaveSpeed AI website.

How are generated files stored and output?

Output mode is controlled by the `WAVESPEED_API_RESOURCE_MODE` environment variable (default: `url`). You can choose `url`, `local`, or `base64`. For local mode, files are saved under the path set by `WAVESPEED_MCP_BASE_PATH` (default: `~/Desktop`).

How do I configure timeouts for API requests and polling?

Set `WAVESPEED_REQUEST_TIMEOUT` (per HTTP request, default 300s) and `WAVESPEED_WAIT_RESULT_TIMEOUT` (total polling time, default 600s) as environment variables. These control how long the server waits for individual calls and the overall job result.

What logging options are available?

By default logs go to console. To enable file logging, set the `WAVESPEED_LOG_FILE` environment variable to a path. The log file uses a rotating handler (max 10 MB, 5 backups) with format `%(asctime)s - wavespeed-mcp - %(levelname)s - %(message)s`. Log level is configurable via `WAVESPEED_LOG_LEVEL`.

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