├── .python-version ├── wavespeed_mcp ├── __init__.py ├── exceptions.py ├── const.py ├── __main__.py ├── client.py ├── utils.py └── server.py ├── setup.py ├── .env.example ├── script └── build.sh ├── wavespeed_mcp_config_demo.json ├── .gitignore ├── pyproject.toml ├── README.zh.md ├── tests ├── test_integration.py └── test_wavespeed_mcp.py ├── README.md └── uv.lock /.python-version: -------------------------------------------------------------------------------- 1 | 3.12 2 | -------------------------------------------------------------------------------- /wavespeed_mcp/__init__.py: -------------------------------------------------------------------------------- 1 | """WavespeedMCP package.""" 2 | 3 | __version__ = "0.1.2" 4 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | 3 | setup( 4 | packages=find_packages(), 5 | include_package_data=True, 6 | ) 7 | -------------------------------------------------------------------------------- /wavespeed_mcp/exceptions.py: -------------------------------------------------------------------------------- 1 | """Custom exceptions for WavespeedMCP.""" 2 | 3 | 4 | class WavespeedAPIError(Exception): 5 | """Base exception for Wavespeed API errors.""" 6 | pass 7 | 8 | 9 | class WavespeedAuthError(WavespeedAPIError): 10 | """Authentication related errors.""" 11 | pass 12 | 13 | 14 | class WavespeedRequestError(WavespeedAPIError): 15 | """Request related errors.""" 16 | pass 17 | 18 | 19 | class WavespeedTimeoutError(WavespeedAPIError): 20 | """Timeout related errors.""" 21 | pass 22 | 23 | 24 | class WavespeedValidationError(WavespeedAPIError): 25 | """Validation related errors.""" 26 | pass 27 | 28 | 29 | class WavespeedMcpError(WavespeedAPIError): 30 | """General MCP related errors.""" 31 | pass 32 | -------------------------------------------------------------------------------- /.env.example: -------------------------------------------------------------------------------- 1 | # WaveSpeed API Configuration 2 | WAVESPEED_API_KEY=your_api_key_here 3 | WAVESPEED_API_HOST=https://api.wavespeed.ai 4 | 5 | # MCP Configuration 6 | WAVESPEED_MCP_BASE_PATH=~/Desktop 7 | WAVESPEED_API_RESOURCE_MODE=url 8 | 9 | # Logging Configuration 10 | WAVESPEED_LOG_LEVEL=INFO 11 | # Optional: Specify log file path (if not set, logs to console) 12 | # WAVESPEED_LOG_FILE=/tmp/wavespeed-mcp.log 13 | # WAVESPEED_LOG_FILE=/var/log/wavespeed-mcp.log 14 | # WAVESPEED_LOG_FILE=~/logs/wavespeed-mcp.log 15 | 16 | # Network Configuration 17 | # Request timeout per HTTP call in seconds (default: 300 = 5 minutes) 18 | # WAVESPEED_REQUEST_TIMEOUT=300 19 | 20 | # Total timeout for waiting/polling results in seconds (default: 600 = 10 minutes) 21 | # WAVESPEED_WAIT_RESULT_TIMEOUT=600 -------------------------------------------------------------------------------- /script/build.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | set -e 4 | 5 | # change to project root 6 | cd "$(dirname "$0")/.." || exit 1 7 | 8 | # show current directory and version 9 | echo "current directory: $(pwd)" 10 | VERSION=$(grep -m 1 "version =" pyproject.toml | cut -d '"' -f2) 11 | echo "build version: $VERSION" 12 | 13 | # clean old build files 14 | echo "clean old build files..." 15 | rm -rf dist/ build/ *.egg-info/ 16 | 17 | # install build tools 18 | echo "install/upgrade build tools..." 19 | pip install --upgrade build twine 20 | 21 | # build package 22 | echo "build distribution package..." 23 | python -m build 24 | 25 | # list built files 26 | echo "built files:" 27 | ls -l dist/ 28 | 29 | # confirm upload 30 | read -p "upload to PyPI? (y/n): " UPLOAD 31 | if [[ "$UPLOAD" == "y" || "$UPLOAD" == "Y" ]]; then 32 | echo "upload to PyPI..." 33 | python -m twine upload dist/* 34 | echo "upload completed!" 35 | else 36 | echo "skip upload." 37 | fi 38 | 39 | echo "build process completed." 40 | -------------------------------------------------------------------------------- /wavespeed_mcp_config_demo.json: -------------------------------------------------------------------------------- 1 | { 2 | "// MCP 服务器配置 - 用于 Claude Desktop 集成": "", 3 | "mcpServers": { 4 | "WaveSpeed": { 5 | "command": "wavespeed-mcp", 6 | "env": { 7 | "WAVESPEED_API_KEY": "your_api_key_here", 8 | "WAVESPEED_API_HOST": "https://api.wavespeed.ai", 9 | "WAVESPEED_MCP_BASE_PATH": "~/Desktop", 10 | "WAVESPEED_API_RESOURCE_MODE": "url", 11 | "WAVESPEED_LOG_LEVEL": "INFO" 12 | } 13 | } 14 | }, 15 | 16 | "// WaveSpeed MCP 服务器配置": "", 17 | "api": { 18 | "key": "your_wavespeed_api_key_here", 19 | "host": "https://api.wavespeed.ai", 20 | "timeout": 60, 21 | "max_retries": -1, 22 | "retry_delay": 1, 23 | "polling_interval": 2, 24 | "endpoints": { 25 | "text_to_image": "/wavespeed-ai/flux-dev", 26 | "image_to_image": "/wavespeed-ai/flux-kontext-pro", 27 | "video": "/wavespeed-ai/wan-2.1/i2v-480p-lora" 28 | } 29 | }, 30 | "output": { 31 | "base_path": "~/Desktop", 32 | "resource_mode": "url" 33 | }, 34 | "logging": { 35 | "level": "INFO" 36 | }, 37 | "telemetry": { 38 | "enabled": true 39 | } 40 | } 41 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Python 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | *.so 6 | .Python 7 | build/ 8 | develop-eggs/ 9 | dist/ 10 | downloads/ 11 | eggs/ 12 | .eggs/ 13 | lib/ 14 | lib64/ 15 | parts/ 16 | sdist/ 17 | var/ 18 | wheels/ 19 | *.egg-info/ 20 | .installed.cfg 21 | *.egg 22 | MANIFEST 23 | 24 | # Virtual Environment 25 | venv/ 26 | env/ 27 | ENV/ 28 | .env 29 | 30 | # IDE and Editors 31 | .idea/ 32 | .vscode/ 33 | *.swp 34 | *.swo 35 | .DS_Store 36 | .project 37 | .pydevproject 38 | .settings/ 39 | 40 | # Logs 41 | logs/ 42 | *.log 43 | npm-debug.log* 44 | yarn-debug.log* 45 | yarn-error.log* 46 | 47 | # Database 48 | *.sqlite3 49 | *.db 50 | 51 | # Local configuration 52 | config.local.yaml 53 | config.local.yml 54 | config.local.json 55 | .env.local 56 | claude_desktop_config.json 57 | 58 | # Testing 59 | .coverage 60 | htmlcov/ 61 | .pytest_cache/ 62 | .tox/ 63 | nosetests.xml 64 | coverage.xml 65 | *.cover 66 | 67 | # Documentation 68 | docs/_build/ 69 | site/ 70 | 71 | # Dependency directories 72 | node_modules/ 73 | jspm_packages/ 74 | 75 | # Distribution / packaging 76 | .Python 77 | env/ 78 | build/ 79 | develop-eggs/ 80 | dist/ 81 | downloads/ 82 | eggs/ 83 | .eggs/ 84 | lib/ 85 | lib64/ 86 | parts/ 87 | sdist/ 88 | var/ 89 | wheels/ 90 | *.egg-info/ 91 | .installed.cfg 92 | *.egg 93 | 94 | # Jupyter Notebook 95 | .ipynb_checkpoints 96 | 97 | # mypy 98 | .mypy_cache/ 99 | 100 | # Temporary files 101 | *.tmp 102 | *.bak 103 | *.swp 104 | *~.nib 105 | example/ -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "wavespeed-mcp" 3 | version = "0.1.27" 4 | description = "WaveSpeed MCP Server" 5 | authors = [ 6 | { name = "Wavespeed AI Team", email = "support@wavespeed.ai" }, 7 | ] 8 | readme = "README.md" 9 | license = { file = "LICENSE" } 10 | classifiers = [ 11 | "Development Status :: 4 - Beta", 12 | "Intended Audience :: Developers", 13 | "License :: OSI Approved :: MIT License", 14 | "Programming Language :: Python :: 3", 15 | "Programming Language :: Python :: 3.11", 16 | "Programming Language :: Python :: 3.12", 17 | ] 18 | keywords = [ 19 | "wavespeed", 20 | "mcp", 21 | "image-generation", 22 | "video-generation", 23 | ] 24 | requires-python = ">=3.11" 25 | dependencies = [ 26 | "mcp[cli]>=1.6.0", 27 | "fastapi==0.109.2", 28 | "uvicorn==0.27.1", 29 | "python-dotenv==1.0.1", 30 | "pydantic>=2.6.1", 31 | "httpx==0.28.1", 32 | "requests==2.31.0", 33 | "tqdm>=4.66.0", 34 | "setuptools>=80.9.0", 35 | ] 36 | 37 | [project.scripts] 38 | wavespeed-mcp = "wavespeed_mcp.server:main" 39 | 40 | [project.optional-dependencies] 41 | dev = [ 42 | "pre-commit==3.6.2", 43 | "ruff==0.3.0", 44 | "fastmcp==0.4.1", 45 | "pytest==8.0.0", 46 | "pytest-cov==4.1.0", 47 | "twine==6.1.0", 48 | "build>=1.0.3", 49 | ] 50 | 51 | [build-system] 52 | requires = ["setuptools>=45", "wheel"] 53 | build-backend = "setuptools.build_meta" 54 | 55 | [tool.pytest.ini_options] 56 | testpaths = ["tests"] 57 | python_files = ["test_*.py"] 58 | addopts = "-v --cov=wavespeed_mcp --cov-report=term-missing" 59 | -------------------------------------------------------------------------------- /wavespeed_mcp/const.py: -------------------------------------------------------------------------------- 1 | """ 2 | Constants for WavespeedMCP. 3 | """ 4 | 5 | # Image generation default values 6 | DEFAULT_IMAGE_SIZE = "1024*1024" 7 | DEFAULT_NUM_INFERENCE_STEPS = 28 8 | DEFAULT_GUIDANCE_SCALE = 3.5 9 | DEFAULT_NUM_IMAGES = 1 10 | DEFAULT_SEED = -1 11 | DEFAULT_STRENGTH = 0.8 12 | DEFAULT_IMAGE_LORA = {"path": "linoyts/yarn_art_Flux_LoRA", "scale": 1.0} 13 | 14 | # Video generation default values 15 | DEFAULT_VIDEO_SIZE = "832*480" 16 | DEFAULT_VIDEO_DURATION = 5 17 | DEFAULT_VIDEO_GUIDANCE_SCALE = 5 18 | DEFAULT_VIDEO_FLOW_SHIFT = 3 19 | DEFAULT_VIDEO_NUM_INFERENCE_STEPS = 30 20 | DEFAULT_VIDEO_LORA = {"path": "Remade-AI/Deflate", "scale": 1.0} 21 | 22 | # Environment variables 23 | ENV_WAVESPEED_API_KEY = "WAVESPEED_API_KEY" 24 | ENV_WAVESPEED_API_HOST = "WAVESPEED_API_HOST" 25 | ENV_WAVESPEED_MCP_BASE_PATH = "WAVESPEED_MCP_BASE_PATH" 26 | # Options: local, url (default) 27 | ENV_RESOURCE_MODE = "WAVESPEED_API_RESOURCE_MODE" 28 | 29 | # Resource modes 30 | RESOURCE_MODE_BASE64 = "base64" # return base64 encoded image 31 | RESOURCE_MODE_LOCAL = "local" # save resource to local file system 32 | RESOURCE_MODE_URL = "url" # provide resource url 33 | 34 | # API endpoints 35 | API_VERSION = "v3" 36 | API_BASE_PATH = "/api" 37 | API_IMAGE_ENDPOINT = "/wavespeed-ai/flux-dev" 38 | API_IMAGE_TO_IMAGE_ENDPOINT = "/wavespeed-ai/flux-kontext-pro" 39 | API_VIDEO_ENDPOINT = "/wavespeed-ai/wan-2.1/i2v-480p-lora" 40 | ENV_API_TEXT_TO_IMAGE_ENDPOINT = "WAVESPEED_API_TEXT_TO_IMAGE_ENDPOINT" 41 | ENV_API_IMAGE_TO_IMAGE_ENDPOINT = "WAVESPEED_API_IMAGE_TO_IMAGE_ENDPOINT" 42 | ENV_API_VIDEO_ENDPOINT = "WAVESPEED_API_VIDEO_ENDPOINT" 43 | API_PREDICTION_ENDPOINT = "/predictions" 44 | 45 | # Logging 46 | DEFAULT_LOG_LEVEL = "INFO" 47 | ENV_FASTMCP_LOG_LEVEL = "WAVESPEED_LOG_LEVEL" 48 | ENV_WAVESPEED_LOG_FILE = "WAVESPEED_LOG_FILE" 49 | 50 | # Network timeout (per HTTP request) 51 | DEFAULT_REQUEST_TIMEOUT = 300 # 5 minutes 52 | ENV_WAVESPEED_REQUEST_TIMEOUT = "WAVESPEED_REQUEST_TIMEOUT" 53 | 54 | # Polling timeout (total wait for result) 55 | DEFAULT_WAIT_RESULT_TIMEOUT = 600 # 10 minutes 56 | ENV_WAVESPEED_WAIT_RESULT_TIMEOUT = "WAVESPEED_WAIT_RESULT_TIMEOUT" 57 | -------------------------------------------------------------------------------- /README.zh.md: -------------------------------------------------------------------------------- 1 | # WavespeedMCP 2 | 3 | WavespeedMCP 是 WaveSpeed AI 服务的模型控制协议（MCP）服务器实现。它通过 MCP 协议为访问 WaveSpeed 的图像和视频生成功能提供了标准化接口。 4 | 5 | ## 功能特点 6 | 7 | - **高级图像生成**：从文本提示创建高质量图像，支持图像到图像生成、局部重绘和 LoRA 模型 8 | - **动态视频生成**：使用可自定义的动作参数将静态图像转换为视频 9 | - **优化性能**：通过智能重试逻辑和详细的进度跟踪增强 API 轮询 10 | - **灵活的资源处理**：支持 URL、Base64 和本地文件输出模式 11 | - **全面的错误处理**：专门的异常层次结构，用于精确识别和恢复错误 12 | - **强大的日志系统**：用于监控和调试的详细日志系统 13 | - **多种配置选项**：支持环境变量、命令行参数和配置文件 14 | 15 | ## 安装 16 | 17 | ### 前提条件 18 | 19 | - Python 3.11+ 20 | - WaveSpeed API 密钥（从 [WaveSpeed AI](https://wavespeed.ai) 获取） 21 | 22 | ### 安装步骤 23 | 24 | 直接从 PyPI 安装： 25 | 26 | ```bash 27 | pip install wavespeed-mcp 28 | ``` 29 | 30 | ### MCP 配置 31 | 32 | 要将 WavespeedMCP 与您的 IDE 或应用程序一起使用，请添加以下配置： 33 | 34 | ```json 35 | { 36 | "mcpServers": { 37 | "Wavespeed": { 38 | "command": "wavespeed-mcp", 39 | "env": { 40 | "WAVESPEED_API_KEY": "wavespeedkey" 41 | } 42 | } 43 | } 44 | } 45 | ``` 46 | 47 | ## 使用方法 48 | 49 | ### 运行服务器 50 | 51 | 启动 WavespeedMCP 服务器： 52 | 53 | ```bash 54 | wavespeed-mcp --api-key your_api_key_here 55 | ``` 56 | 57 | ### Claude Desktop 集成 58 | 59 | WavespeedMCP 可以与 Claude Desktop 集成。要生成必要的配置文件： 60 | 61 | ```bash 62 | python -m wavespeed_mcp --api-key your_api_key_here --config-path /path/to/claude/config 63 | ``` 64 | 65 | 此命令生成一个 `claude_desktop_config.json` 文件，该文件配置 Claude Desktop 使用 WavespeedMCP 工具。生成配置后： 66 | 67 | 1. 使用 `wavespeed-mcp` 命令启动 WavespeedMCP 服务器 68 | 2. 启动 Claude Desktop，它将使用配置好的 WavespeedMCP 工具 69 | 70 | ## 配置选项 71 | 72 | WavespeedMCP 可以通过以下方式进行配置： 73 | 74 | 1. **环境变量**： 75 | 76 | - `WAVESPEED_API_KEY`：您的 WaveSpeed API 密钥（必需） 77 | - `WAVESPEED_API_HOST`：API 主机 URL（默认：https://api.wavespeed.ai） 78 | - `WAVESPEED_MCP_BASE_PATH`：输出文件的基本路径（默认：~/Desktop） 79 | - `WAVESPEED_API_RESOURCE_MODE`：资源输出模式（选项：url、base64、local；默认：url） 80 | - `WAVESPEED_LOG_LEVEL`：日志级别（选项：DEBUG、INFO、WARNING、ERROR；默认：INFO） 81 | - `WAVESPEED_API_TEXT_TO_IMAGE_ENDPOINT`：文本生成图像的自定义端点（默认：/wavespeed-ai/flux-dev） 82 | - `WAVESPEED_API_IMAGE_TO_IMAGE_ENDPOINT`：图像编辑的自定义端点（默认：/wavespeed-ai/flux-kontext-pro） 83 | - `WAVESPEED_API_VIDEO_ENDPOINT`：视频生成的自定义端点（默认：/wavespeed-ai/wan-2.1/i2v-480p-lora） 84 | 85 | 2. **命令行参数**： 86 | 87 | - `--api-key`：您的 WaveSpeed API 密钥 88 | - `--api-host`：API 主机 URL 89 | - `--config`：配置文件的路径 90 | 91 | 3. **配置文件**（JSON 格式）： 92 | 参见 `wavespeed_mcp_config_demo.json` 示例。 93 | 94 | ## 架构 95 | 96 | WavespeedMCP 遵循清晰、模块化的架构： 97 | 98 | - `server.py`：核心 MCP 服务器实现，包含工具定义 99 | - `client.py`：优化的 API 客户端，具有智能轮询功能 100 | - `utils.py`：用于资源处理的综合实用函数 101 | - `exceptions.py`：用于错误处理的专门异常层次结构 102 | - `const.py`：常量和默认配置值 103 | 104 | ## 开发 105 | 106 | ### 要求 107 | 108 | - Python 3.11+ 109 | - 开发依赖：`pip install -e ".[dev]"` 110 | 111 | ### 测试 112 | 113 | 运行测试套件： 114 | 115 | ```bash 116 | pytest 117 | ``` 118 | 119 | 或者使用覆盖率报告： 120 | 121 | ```bash 122 | pytest --cov=wavespeed_mcp 123 | ``` 124 | 125 | ## 许可证 126 | 127 | 本项目根据 MIT 许可证授权 - 有关详细信息，请参阅 LICENSE 文件。 128 | 129 | ## 支持 130 | 131 | 如需支持或功能请求，请联系 WaveSpeed AI 团队：support@wavespeed.ai。 132 | -------------------------------------------------------------------------------- /wavespeed_mcp/__main__.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | from pathlib import Path 4 | import sys 5 | from dotenv import load_dotenv 6 | import argparse 7 | 8 | load_dotenv() 9 | 10 | 11 | def get_claude_config_path() -> Path | None: 12 | """Get the Claude config directory based on platform.""" 13 | if sys.platform == "win32": 14 | path = Path(Path.home(), "AppData", "Roaming", "Claude") 15 | elif sys.platform == "darwin": 16 | path = Path(Path.home(), "Library", "Application Support", "Claude") 17 | elif sys.platform.startswith("linux"): 18 | path = Path( 19 | os.environ.get("XDG_CONFIG_HOME", Path.home() / ".config"), "Claude" 20 | ) 21 | else: 22 | return None 23 | 24 | if path.exists(): 25 | return path 26 | return None 27 | 28 | 29 | def get_python_path(): 30 | return sys.executable 31 | 32 | 33 | def generate_config(api_key: str | None = None): 34 | """Generate Claude config file.""" 35 | final_api_key = api_key or os.environ.get("WAVESPEED_API_KEY") 36 | if not final_api_key: 37 | print("Error: WaveSpeed API key is required.") 38 | print("Please either:") 39 | print(" 1. Pass the API key using --api-key argument, or") 40 | print(" 2. Set the WAVESPEED_API_KEY environment variable, or") 41 | print(" 3. Add WAVESPEED_API_KEY to your .env file") 42 | sys.exit(1) 43 | 44 | config = { 45 | "mcpServers": { 46 | "WaveSpeed": { 47 | "command": "wavespeed-mcp", 48 | "env": { 49 | "WAVESPEED_API_KEY": final_api_key, 50 | "WAVESPEED_MCP_BASE_PATH": "", 51 | "WAVESPEED_API_HOST": "https://api.wavespeed.ai", 52 | }, 53 | } 54 | } 55 | } 56 | 57 | return config 58 | 59 | 60 | if __name__ == "__main__": 61 | parser = argparse.ArgumentParser() 62 | parser.add_argument( 63 | "--print", 64 | action="store_true", 65 | help="Print config to screen instead of writing to file", 66 | ) 67 | parser.add_argument( 68 | "--api-key", 69 | help="WaveSpeed API key (alternatively, set WAVESPEED_API_KEY environment variable)", 70 | ) 71 | parser.add_argument( 72 | "--config-path", 73 | type=Path, 74 | help="Custom path to Claude config directory", 75 | ) 76 | args = parser.parse_args() 77 | 78 | config = generate_config(args.api_key) 79 | 80 | if args.print: 81 | print(json.dumps(config, indent=2)) 82 | else: 83 | claude_path = args.config_path if args.config_path else get_claude_config_path() 84 | if claude_path is None: 85 | print( 86 | "Could not find Claude config path automatically. Please specify it using --config-path argument. The argument should be an absolute path of the claude_desktop_config.json file." 87 | ) 88 | sys.exit(1) 89 | 90 | claude_path.mkdir(parents=True, exist_ok=True) 91 | print("Writing config to", claude_path / "claude_desktop_config.json") 92 | with open(claude_path / "claude_desktop_config.json", "w") as f: 93 | json.dump(config, f, indent=2) 94 | -------------------------------------------------------------------------------- /tests/test_integration.py: -------------------------------------------------------------------------------- 1 | """Integration tests for WavespeedMCP. 2 | 3 | These tests demonstrate how to use the WavespeedMCP service in a real-world scenario. 4 | Note: These tests require a valid WaveSpeed API key to run. 5 | """ 6 | 7 | import os 8 | import asyncio 9 | import unittest 10 | import json 11 | from dotenv import load_dotenv 12 | from mcp import ClientSession 13 | from mcp.client.stdio import stdio_client 14 | from mcp import StdioServerParameters 15 | 16 | 17 | class TestWavespeedIntegration(unittest.TestCase): 18 | """Integration tests for WavespeedMCP.""" 19 | 20 | @classmethod 21 | def setUpClass(cls): 22 | """Set up the test environment.""" 23 | load_dotenv() 24 | cls.api_key = os.getenv("WAVESPEED_API_KEY") 25 | if not cls.api_key: 26 | raise unittest.SkipTest("WAVESPEED_API_KEY environment variable not set") 27 | 28 | async def _run_client(self, tool_name, params): 29 | """Run the client with the specified tool and parameters.""" 30 | server_params = StdioServerParameters( 31 | command="wavespeed-mcp", args=["--api-key", self.api_key] 32 | ) 33 | print(f"Running client with parameters: {server_params}") 34 | async with stdio_client(server_params) as (read_stream, write_stream): 35 | async with ClientSession(read_stream, write_stream) as client: 36 | await client.initialize() 37 | 38 | # Get available tools 39 | tools_result = await client.list_tools() 40 | tools = tools_result.tools 41 | 42 | # Verify tool is available 43 | tool_names = [tool.name for tool in tools] 44 | self.assertIn(tool_name, tool_names) 45 | 46 | # Call the tool 47 | result = await client.call_tool(tool_name, params) 48 | return result 49 | 50 | def test_generate_image(self): 51 | """Test generating an image.""" 52 | params = { 53 | "prompt": "A beautiful mountain landscape with a lake", 54 | "size": "512*512", # Smaller size for faster testing 55 | "num_images": 1, 56 | } 57 | 58 | print("Calling API with params:", params) 59 | result = asyncio.run(self._run_client("text_to_image", params)) 60 | print("Received API response: ", result) 61 | 62 | # Verify result 63 | print("Verifying result...") 64 | self.assertIsNotNone(result) 65 | self.assertTrue(len(result.content) > 0) 66 | 67 | # 验证返回的内容 68 | content = result.content[0] 69 | self.assertEqual(content.type, "text") 70 | self.assertTrue(content.text) 71 | 72 | # 解析 JSON 字符串 73 | data = json.loads(content.text) 74 | self.assertEqual(data["status"], "success") 75 | self.assertIn("urls", data) 76 | self.assertIsInstance(data["urls"], list) 77 | self.assertGreater(len(data["urls"]), 0) 78 | url = data["urls"][0] 79 | self.assertTrue(url.startswith("http")) 80 | self.assertIsNone(data["error"]) 81 | print(f"Generated image URL: {url}") 82 | 83 | def test_generate_video(self): 84 | """Test generating a video.""" 85 | # For testing, we'll use a sample image URL 86 | # In a real test, you would use a valid image URL 87 | params = { 88 | "image": "https://d2p7pge43lyniu.cloudfront.net/output/b4d0ccc2-a2f4-4495-b4a8-fcbb60d3ab82-u2_0af704ff-9f76-4b06-8d94-97b1975ff604.jpeg", 89 | "prompt": "A peaceful mountain scene with gentle wind", 90 | "duration": 5, # Short duration for faster testing 91 | } 92 | 93 | result = asyncio.run(self._run_client("generate_video", params)) 94 | 95 | # Verify result 96 | self.assertIsNotNone(result) 97 | 98 | # Check if we got a text response with a video URL 99 | # Verify result 100 | print("Verifying result...:", result) 101 | self.assertIsNotNone(result) 102 | self.assertTrue(len(result.content) > 0) 103 | 104 | 105 | if __name__ == "__main__": 106 | unittest.main() 107 | -------------------------------------------------------------------------------- /tests/test_wavespeed_mcp.py: -------------------------------------------------------------------------------- 1 | """Unit tests for WavespeedMCP.""" 2 | 3 | import os 4 | import unittest 5 | from unittest.mock import patch, MagicMock 6 | from pathlib import Path 7 | 8 | from wavespeed_mcp.utils import ( 9 | build_output_path, 10 | build_output_file, 11 | validate_loras 12 | ) 13 | from wavespeed_mcp.exceptions import WavespeedMcpError 14 | 15 | 16 | class TestWavespeedUtils(unittest.TestCase): 17 | """Test utility functions.""" 18 | 19 | def test_build_output_file(self): 20 | """Test build_output_file function.""" 21 | tool = "test_tool" 22 | description = "test description" 23 | output_path = Path("/tmp") 24 | extension = "png" 25 | 26 | filename = build_output_file(tool, description, output_path, extension) 27 | 28 | self.assertTrue(filename.startswith(f"{tool}_test_description_")) 29 | self.assertTrue(filename.endswith(f".{extension}")) 30 | 31 | @patch('wavespeed_mcp.utils.is_file_writeable') 32 | def test_build_output_path_default(self, mock_is_writeable): 33 | """Test build_output_path with default values.""" 34 | mock_is_writeable.return_value = True 35 | 36 | with patch('pathlib.Path.mkdir') as mock_mkdir: 37 | path = build_output_path() 38 | 39 | self.assertEqual(path, Path.home() / "Desktop") 40 | mock_mkdir.assert_called_once_with(parents=True, exist_ok=True) 41 | 42 | @patch('wavespeed_mcp.utils.is_file_writeable') 43 | def test_build_output_path_custom(self, mock_is_writeable): 44 | """Test build_output_path with custom directory.""" 45 | mock_is_writeable.return_value = True 46 | 47 | with patch('pathlib.Path.mkdir') as mock_mkdir: 48 | path = build_output_path("/custom/path") 49 | 50 | self.assertEqual(path, Path("/custom/path")) 51 | mock_mkdir.assert_called_once_with(parents=True, exist_ok=True) 52 | 53 | @patch('wavespeed_mcp.utils.is_file_writeable') 54 | def test_build_output_path_not_writeable(self, mock_is_writeable): 55 | """Test build_output_path with non-writeable directory.""" 56 | mock_is_writeable.return_value = False 57 | 58 | with self.assertRaises(WavespeedMcpError): 59 | build_output_path("/non/writeable/path") 60 | 61 | def test_validate_loras_valid(self): 62 | """Test validate_loras with valid loras.""" 63 | loras = [ 64 | {"path": "test/path", "scale": 1.0}, 65 | {"path": "another/path"} 66 | ] 67 | 68 | result = validate_loras(loras) 69 | 70 | self.assertEqual(len(result), 2) 71 | self.assertEqual(result[0]["path"], "test/path") 72 | self.assertEqual(result[0]["scale"], 1.0) 73 | self.assertEqual(result[1]["path"], "another/path") 74 | self.assertEqual(result[1]["scale"], 1.0) # Default scale added 75 | 76 | def test_validate_loras_invalid(self): 77 | """Test validate_loras with invalid loras.""" 78 | # Missing path 79 | with self.assertRaises(WavespeedMcpError): 80 | validate_loras([{"scale": 1.0}]) 81 | 82 | # Not a dict 83 | with self.assertRaises(WavespeedMcpError): 84 | validate_loras(["not a dict"]) 85 | 86 | def test_validate_loras_empty(self): 87 | """Test validate_loras with empty loras.""" 88 | self.assertEqual(validate_loras([]), []) 89 | self.assertEqual(validate_loras(None), []) 90 | 91 | 92 | class TestWavespeedClient(unittest.TestCase): 93 | """Test API client.""" 94 | 95 | @patch('wavespeed_mcp.client.requests.Session') 96 | def test_client_initialization(self, mock_session): 97 | """Test client initialization.""" 98 | from wavespeed_mcp.client import WavespeedAPIClient 99 | 100 | api_key = "test_key" 101 | api_host = "https://test.host" 102 | 103 | client = WavespeedAPIClient(api_key, api_host) 104 | 105 | self.assertEqual(client.api_key, api_key) 106 | self.assertEqual(client.api_host, api_host) 107 | 108 | # Check headers 109 | headers = mock_session.return_value.headers.update.call_args[0][0] 110 | self.assertEqual(headers["Authorization"], f"Bearer {api_key}") 111 | self.assertEqual(headers["Content-Type"], "application/json") 112 | 113 | 114 | if __name__ == "__main__": 115 | unittest.main() 116 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # WavespeedMCP 2 | 3 | ## [English](README.md) ｜ [中文文档](README.zh.md) 4 | 5 | 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. 6 | 7 | ## Features 8 | 9 | - **Advanced Image Generation**: Create high-quality images from text prompts with support for image-to-image generation, inpainting, and LoRA models 10 | - **Dynamic Video Generation**: Transform static images into videos with customizable motion parameters 11 | - **Optimized Performance**: Enhanced API polling with intelligent retry logic and detailed progress tracking 12 | - **Flexible Resource Handling**: Support for URL, Base64, and local file output modes 13 | - **Comprehensive Error Handling**: Specialized exception hierarchy for precise error identification and recovery 14 | - **Robust Logging**: Detailed logging system for monitoring and debugging 15 | - **Multiple Configuration Options**: Support for environment variables, command-line arguments, and configuration files 16 | 17 | ## Installation 18 | 19 | ### Prerequisites 20 | 21 | - Python 3.11+ 22 | - WaveSpeed API key (obtain from [WaveSpeed AI](https://wavespeed.ai)) 23 | 24 | ### Setup 25 | 26 | Install directly from PyPI: 27 | 28 | ```bash 29 | pip install wavespeed-mcp 30 | ``` 31 | 32 | ### MCP Configuration 33 | 34 | To use WavespeedMCP with your IDE or application, add the following configuration: 35 | 36 | ```json 37 | { 38 | "mcpServers": { 39 | "WaveSpeed": { 40 | "command": "wavespeed-mcp", 41 | "env": { 42 | "WAVESPEED_API_KEY": "your-api-key-here", 43 | "WAVESPEED_LOG_FILE": "/tmp/wavespeed-mcp.log" 44 | } 45 | } 46 | } 47 | } 48 | ``` 49 | 50 | ## Usage 51 | 52 | ### Running the Server 53 | 54 | Start the WavespeedMCP server: 55 | 56 | ```bash 57 | wavespeed-mcp --api-key your_api_key_here 58 | ``` 59 | 60 | ### Claude Desktop Integration 61 | 62 | WavespeedMCP can be integrated with Claude Desktop. To generate the necessary configuration file: 63 | 64 | ```bash 65 | python -m wavespeed_mcp --api-key your_api_key_here --config-path /path/to/claude/config 66 | ``` 67 | 68 | This command generates a `claude_desktop_config.json` file that configures Claude Desktop to use WavespeedMCP tools. After generating the configuration: 69 | 70 | 1. Start the WavespeedMCP server using the `wavespeed-mcp` command 71 | 2. Launch Claude Desktop, which will use the configured WavespeedMCP tools 72 | 73 | ## Configuration Options 74 | 75 | WavespeedMCP can be configured through: 76 | 77 | 1. **Environment Variables**: 78 | 79 | - `WAVESPEED_API_KEY`: Your WaveSpeed API key (required) 80 | - `WAVESPEED_API_HOST`: API host URL (default: https://api.wavespeed.ai) 81 | - `WAVESPEED_MCP_BASE_PATH`: Base path for saving generated files (default: ~/Desktop) 82 | - `WAVESPEED_API_RESOURCE_MODE`: Resource output mode - `url`, `local`, or `base64` (default: url) 83 | - `WAVESPEED_LOG_LEVEL`: Logging level - DEBUG, INFO, WARNING, ERROR (default: INFO) 84 | - `WAVESPEED_LOG_FILE`: Optional log file path (if not set, logs to console) 85 | - `WAVESPEED_API_TEXT_TO_IMAGE_ENDPOINT`: Custom endpoint for text-to-image generation (default: /wavespeed-ai/flux-dev) 86 | - `WAVESPEED_API_IMAGE_TO_IMAGE_ENDPOINT`: Custom endpoint for image-to-image generation (default: /wavespeed-ai/flux-kontext-pro) 87 | - `WAVESPEED_API_VIDEO_ENDPOINT`: Custom endpoint for video generation (default: /wavespeed-ai/wan-2.1/i2v-480p-lora) 88 | 89 | ### Timeouts 90 | 91 | WavespeedMCP supports two types of timeouts. Configure them via environment variables: 92 | 93 | - `WAVESPEED_REQUEST_TIMEOUT`: Per-HTTP request timeout in seconds (default: 300 = 5 minutes). 94 | This applies to individual HTTP calls made by the client, such as submitting a job or downloading outputs. 95 | 96 | - `WAVESPEED_WAIT_RESULT_TIMEOUT`: Total timeout for waiting/polling results in seconds (default: 600 = 10 minutes). 97 | This limits the overall time spent polling for an asynchronous job result. When exceeded, polling stops with a timeout error. 98 | 99 | Example: 100 | 101 | ```bash 102 | export WAVESPEED_REQUEST_TIMEOUT=300 # per HTTP request 103 | export WAVESPEED_WAIT_RESULT_TIMEOUT=900 # total wait for result (polling) 104 | ``` 105 | 106 | ### Logging Configuration 107 | 108 | By default, the MCP server logs to console. You can configure file logging by setting the `WAVESPEED_LOG_FILE` environment variable: 109 | 110 | ```bash 111 | # Log to /tmp directory 112 | export WAVESPEED_LOG_FILE=/tmp/wavespeed-mcp.log 113 | 114 | # Log to system log directory 115 | export WAVESPEED_LOG_FILE=/var/log/wavespeed-mcp.log 116 | 117 | # Log to user home directory 118 | export WAVESPEED_LOG_FILE=~/logs/wavespeed-mcp.log 119 | ``` 120 | 121 | The log file uses rotating file handler with: 122 | - Maximum file size: 10MB 123 | - Backup count: 5 files 124 | - Log format: `%(asctime)s - wavespeed-mcp - %(levelname)s - %(message)s` 125 | 126 | 2. **Command-line Arguments**: 127 | 128 | - `--api-key`: Your WaveSpeed API key 129 | - `--api-host`: API host URL 130 | - `--config`: Path to configuration file 131 | 132 | 3. **Configuration File** (JSON format): 133 | See `wavespeed_mcp_config_demo.json` for an example. 134 | 135 | ## Architecture 136 | 137 | WavespeedMCP follows a clean, modular architecture: 138 | 139 | - `server.py`: Core MCP server implementation with tool definitions 140 | - `client.py`: Optimized API client with intelligent polling 141 | - `utils.py`: Comprehensive utility functions for resource handling 142 | - `exceptions.py`: Specialized exception hierarchy for error handling 143 | - `const.py`: Constants and default configuration values 144 | 145 | ## Development 146 | 147 | ### Requirements 148 | 149 | - Python 3.11+ 150 | - Development dependencies: `pip install -e ".[dev]"` 151 | 152 | ### Testing 153 | 154 | Run the test suite: 155 | 156 | ```bash 157 | pytest 158 | ``` 159 | 160 | Or with coverage reporting: 161 | 162 | ```bash 163 | pytest --cov=wavespeed_mcp 164 | ``` 165 | 166 | ## License 167 | 168 | This project is licensed under the MIT License - see the LICENSE file for details. 169 | 170 | ## Support 171 | 172 | For support or feature requests, please contact the WaveSpeed AI team at support@wavespeed.ai. 173 | -------------------------------------------------------------------------------- /wavespeed_mcp/client.py: -------------------------------------------------------------------------------- 1 | """WaveSpeed API client base class.""" 2 | 3 | import time 4 | import os 5 | import requests 6 | import logging 7 | from typing import Dict, Any 8 | 9 | from wavespeed_mcp.exceptions import ( 10 | WavespeedAuthError, 11 | WavespeedRequestError, 12 | WavespeedTimeoutError, 13 | ) 14 | from wavespeed_mcp.const import ( 15 | API_PREDICTION_ENDPOINT, 16 | DEFAULT_WAIT_RESULT_TIMEOUT, 17 | ENV_WAVESPEED_WAIT_RESULT_TIMEOUT, 18 | ) 19 | 20 | logger = logging.getLogger("wavespeed-client") 21 | 22 | 23 | class WavespeedAPIClient: 24 | """Base client for making requests to WaveSpeed API.""" 25 | 26 | def __init__(self, api_key: str, api_host: str): 27 | """Initialize the API client. 28 | 29 | Args: 30 | api_key: The API key for authentication 31 | api_host: The API host URL 32 | """ 33 | self.api_key = api_key 34 | self.api_host = api_host 35 | self.session = requests.Session() 36 | self.session.headers.update( 37 | {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"} 38 | ) 39 | 40 | def _make_request(self, method: str, endpoint: str, **kwargs) -> Dict[str, Any]: 41 | """Make an HTTP request to the WaveSpeed API. 42 | 43 | Args: 44 | method: HTTP method (GET, POST, etc.) 45 | endpoint: API endpoint path 46 | **kwargs: Additional arguments to pass to requests 47 | 48 | Returns: 49 | API response data as dictionary 50 | 51 | Raises: 52 | WavespeedAuthError: If authentication fails 53 | WavespeedRequestError: If the request fails 54 | """ 55 | host = self.api_host.rstrip("/") 56 | path = endpoint if endpoint.startswith("/") else f"/{endpoint}" 57 | url = f"{host}{path}" 58 | 59 | logger.debug(f"Making {method} request to {url}") 60 | 61 | # Add timeout to prevent hanging 62 | if "timeout" not in kwargs: 63 | from wavespeed_mcp.const import ( 64 | DEFAULT_REQUEST_TIMEOUT, 65 | ENV_WAVESPEED_REQUEST_TIMEOUT, 66 | ) 67 | import os 68 | 69 | timeout = int( 70 | os.getenv(ENV_WAVESPEED_REQUEST_TIMEOUT, DEFAULT_REQUEST_TIMEOUT) 71 | ) 72 | kwargs["timeout"] = timeout 73 | 74 | response = None 75 | try: 76 | response = self.session.request(method, url, **kwargs) 77 | 78 | # Check for HTTP errors 79 | response.raise_for_status() 80 | 81 | data = response.json() 82 | 83 | # Check for API-specific errors 84 | if "error" in data: 85 | error_msg = data.get("error", "Unknown API error") 86 | raise WavespeedRequestError(f"API Error: {error_msg}") 87 | 88 | return data 89 | 90 | except requests.exceptions.Timeout: 91 | timeout = kwargs.get("timeout", 300) 92 | raise WavespeedTimeoutError( 93 | f"Request to {url} timed out after {timeout} seconds" 94 | ) 95 | except requests.exceptions.RequestException as e: 96 | if response is not None: 97 | if response.status_code == 401: 98 | raise WavespeedAuthError(f"Authentication failed: {str(e)}") 99 | if hasattr(response, "text") and response.text: 100 | raise WavespeedRequestError(f"Request failed: {response.text}") 101 | raise WavespeedRequestError(f"Request failed: {str(e)}") 102 | 103 | def get(self, endpoint: str, **kwargs) -> Dict[str, Any]: 104 | """Make a GET request.""" 105 | return self._make_request("GET", endpoint, **kwargs) 106 | 107 | def post(self, endpoint: str, **kwargs) -> Dict[str, Any]: 108 | """Make a POST request.""" 109 | return self._make_request("POST", endpoint, **kwargs) 110 | 111 | def poll_result( 112 | self, 113 | wavespeed_request_id: str, 114 | max_retries: int = -1, 115 | poll_interval: float = 0.5, 116 | request_id: str = None, 117 | total_timeout: float = None, 118 | ) -> Dict[str, Any]: 119 | """Poll for the result of an asynchronous API request. 120 | 121 | Args: 122 | wavespeed_request_id: The WaveSpeed API request ID to poll for 123 | max_retries: Maximum number of polling attempts. -1 for infinite retries. 124 | poll_interval: Time in seconds between polling attempts 125 | request_id: Optional MCP request ID for logging correlation 126 | total_timeout: Optional maximum total polling time in seconds. If not provided, 127 | will use ENV_WAVESPEED_REQUEST_TIMEOUT or DEFAULT_REQUEST_TIMEOUT. 128 | 129 | Returns: 130 | The final result of the API request 131 | 132 | Raises: 133 | WavespeedTimeoutError: If polling exceeds max_retries 134 | WavespeedRequestError: If the request fails 135 | """ 136 | result_url = f"{API_PREDICTION_ENDPOINT}/{wavespeed_request_id}/result" 137 | 138 | attempt = 0 139 | 140 | log_prefix = f"[{request_id}]" if request_id else "" 141 | logger.info( 142 | f"{log_prefix} Starting API polling for WaveSpeed ID: {wavespeed_request_id}" 143 | ) 144 | 145 | start_time = time.time() 146 | # Resolve total timeout using WAIT_RESULT env/default 147 | if total_timeout is None: 148 | try: 149 | total_timeout = float( 150 | os.getenv( 151 | ENV_WAVESPEED_WAIT_RESULT_TIMEOUT, DEFAULT_WAIT_RESULT_TIMEOUT 152 | ) 153 | ) 154 | except Exception: 155 | total_timeout = float(DEFAULT_WAIT_RESULT_TIMEOUT) 156 | 157 | while True: 158 | if max_retries != -1 and attempt >= max_retries: 159 | elapsed = time.time() - start_time 160 | logger.error( 161 | f"{log_prefix} API polling timed out after {max_retries} attempts ({elapsed:.1f}s)" 162 | ) 163 | raise WavespeedTimeoutError( 164 | f"Polling timed out after {max_retries} attempts" 165 | ) 166 | 167 | # Check total timeout window 168 | elapsed_total = time.time() - start_time 169 | if total_timeout is not None and total_timeout > 0 and elapsed_total >= total_timeout: 170 | logger.error( 171 | ( 172 | f"{log_prefix} API polling exceeded total timeout of " 173 | f"{total_timeout:.1f}s after {attempt+1} attempts" 174 | ) 175 | ) 176 | raise WavespeedTimeoutError( 177 | f"Polling timed out after {total_timeout:.1f} seconds" 178 | ) 179 | 180 | try: 181 | # Reduce log frequency - only log every 20 attempts (10 seconds) 182 | if attempt % 20 == 0 and attempt > 0: 183 | logger.debug( 184 | ( 185 | f"{log_prefix} Polling attempt {attempt+1}/" 186 | f"{max_retries if max_retries != -1 else '∞'} " 187 | f"({time.time() - start_time:.1f}s elapsed)" 188 | ) 189 | ) 190 | 191 | response = self.get(result_url) 192 | result = response.get("data", {}) 193 | status = result.get("status") 194 | 195 | # Only log status changes, not every poll 196 | if not hasattr(self, "_last_status") or self._last_status != status: 197 | logger.debug(f"{log_prefix} Status changed to: {status}") 198 | self._last_status = status 199 | 200 | if status == "completed": 201 | elapsed = time.time() - start_time 202 | logger.info( 203 | f"{log_prefix} API polling completed after {attempt+1} attempts ({elapsed:.1f}s)" 204 | ) 205 | return result 206 | elif status == "failed": 207 | elapsed = time.time() - start_time 208 | logger.error( 209 | f"{log_prefix} API polling failed after {attempt+1} attempts ({elapsed:.1f}s)" 210 | ) 211 | error = result.get("error", "unknown error") 212 | raise WavespeedRequestError(f"API request failed: {error}") 213 | 214 | # If still processing, wait and try again 215 | time.sleep(poll_interval) 216 | attempt += 1 217 | 218 | except WavespeedRequestError as e: 219 | # If it's a request error, re-raise it 220 | elapsed = time.time() - start_time 221 | logger.error(f"{log_prefix} Request failed: {str(e)}") 222 | raise 223 | except Exception as e: 224 | # For other exceptions, log and continue polling 225 | logger.warning(f"{log_prefix} Error during polling: {str(e)}") 226 | -------------------------------------------------------------------------------- /wavespeed_mcp/utils.py: -------------------------------------------------------------------------------- 1 | """Utility functions for WavespeedMCP.""" 2 | 3 | import os 4 | import logging 5 | from pathlib import Path 6 | from datetime import datetime 7 | from typing import Dict, List, Optional, Union 8 | from wavespeed_mcp.exceptions import WavespeedMcpError 9 | 10 | 11 | logger = logging.getLogger("wavespeed-utils") 12 | 13 | 14 | def is_file_writeable(path: Path) -> bool: 15 | """Check if a file or directory is writeable. 16 | 17 | Args: 18 | path: Path to check 19 | 20 | Returns: 21 | True if the path is writeable, False otherwise 22 | """ 23 | if path.exists(): 24 | return os.access(path, os.W_OK) 25 | parent_dir = path.parent 26 | return os.access(parent_dir, os.W_OK) 27 | 28 | 29 | def build_output_file( 30 | tool: str, description: str, output_path: Path, extension: str 31 | ) -> Path: 32 | """Build an output filename based on the tool and description. 33 | 34 | Args: 35 | tool: Name of the tool generating the file 36 | description: Brief description to include in the filename 37 | output_path: Directory to save the file in 38 | extension: File extension 39 | 40 | Returns: 41 | Path object for the output file 42 | """ 43 | # Use a short version of the description for the filename 44 | short_desc = description[:20].replace(" ", "_") 45 | 46 | # Create a filename with timestamp 47 | output_file_name = ( 48 | f"{tool}_{short_desc}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.{extension}" 49 | ) 50 | # Return the full path (output_path / filename) 51 | return output_path / output_file_name 52 | 53 | 54 | def build_output_path( 55 | output_directory: Optional[str] = None, base_path: Optional[str] = None 56 | ) -> Path: 57 | """Build the output path for saving files. 58 | 59 | Args: 60 | output_directory: User-specified output directory 61 | base_path: Base path to use if output_directory is not absolute 62 | 63 | Returns: 64 | Path object for the output directory 65 | 66 | Raises: 67 | WavespeedMcpError: If the directory is not writeable 68 | """ 69 | # Set default base_path to desktop if not provided 70 | if base_path is None: 71 | base_path = str(Path.home() / "Desktop") 72 | 73 | # Ensure base_path is expanded properly 74 | expanded_base_path = os.path.expanduser(base_path) 75 | 76 | # Handle output path based on output_directory 77 | if output_directory is None: 78 | # If no output directory specified, use the expanded base path 79 | output_path = Path(expanded_base_path) 80 | elif not os.path.isabs(os.path.expanduser(output_directory)): 81 | # For relative paths, join with the expanded base path 82 | output_path = Path(expanded_base_path) / Path(output_directory) 83 | else: 84 | # For absolute paths, use the expanded output directory 85 | output_path = Path(os.path.expanduser(output_directory)) 86 | 87 | # Safety checks and directory creation 88 | if not is_file_writeable(output_path): 89 | raise WavespeedMcpError(f"Directory ({output_path}) is not writeable") 90 | 91 | output_path.mkdir(parents=True, exist_ok=True) 92 | return output_path 93 | 94 | 95 | def save_image_from_url(url: str, output_path: Path, filename: str) -> Path: 96 | """Download and save an image from a URL. 97 | 98 | Args: 99 | url: URL of the image to download 100 | output_path: Directory to save the image in 101 | filename: Filename to save the image as 102 | 103 | Returns: 104 | Path object for the saved image 105 | 106 | Raises: 107 | WavespeedMcpError: If the image cannot be downloaded or saved 108 | """ 109 | import requests 110 | 111 | try: 112 | response = requests.get(url, stream=True) 113 | response.raise_for_status() 114 | 115 | full_path = output_path / filename 116 | with open(full_path, "wb") as f: 117 | for chunk in response.iter_content(chunk_size=8192): 118 | f.write(chunk) 119 | 120 | return full_path 121 | 122 | except Exception as e: 123 | raise WavespeedMcpError(f"Failed to save image from URL: {str(e)}") 124 | 125 | 126 | def save_base64_image(base64_data: str, output_path: Path, filename: str) -> Path: 127 | """Save a base64 encoded image to a file. 128 | 129 | Args: 130 | base64_data: Base64 encoded image data 131 | output_path: Directory to save the image in 132 | filename: Filename to save the image as 133 | 134 | Returns: 135 | Path object for the saved image 136 | 137 | Raises: 138 | WavespeedMcpError: If the image cannot be saved 139 | """ 140 | import base64 141 | 142 | try: 143 | # Create the output directory if it doesn't exist 144 | output_path.mkdir(parents=True, exist_ok=True) 145 | 146 | # Decode the base64 data 147 | image_data = base64.b64decode(base64_data) 148 | 149 | # Save the image 150 | full_path = output_path / filename 151 | with open(full_path, "wb") as f: 152 | f.write(image_data) 153 | 154 | return full_path 155 | 156 | except Exception as e: 157 | raise WavespeedMcpError(f"Failed to save base64 image: {str(e)}") 158 | 159 | 160 | def get_image_as_base64(url: str, timeout: int = 30) -> tuple[str, str]: 161 | """Download an image from a URL and convert it to base64. 162 | 163 | Args: 164 | url: URL of the image to download 165 | timeout: Request timeout in seconds 166 | 167 | Returns: 168 | Tuple of (base64_string, mime_type) 169 | 170 | Raises: 171 | WavespeedMcpError: If the image cannot be downloaded or converted 172 | """ 173 | import requests 174 | import base64 175 | 176 | try: 177 | response = requests.get(url, stream=True, timeout=timeout) 178 | response.raise_for_status() 179 | 180 | # Get content type 181 | content_type = response.headers.get("content-type", "image/jpeg") 182 | if not content_type.startswith("image/"): 183 | raise WavespeedMcpError(f"Invalid content type: {content_type}") 184 | 185 | # Get image data 186 | image_data = response.content 187 | 188 | # Convert to base64 189 | base64_data = base64.b64encode(image_data).decode("utf-8") 190 | 191 | return base64_data, content_type 192 | 193 | except requests.Timeout: 194 | raise WavespeedMcpError( 195 | f"Timeout downloading image from URL (after {timeout}s)" 196 | ) 197 | except requests.RequestException as e: 198 | raise WavespeedMcpError(f"Failed to download image: {str(e)}") 199 | except Exception as e: 200 | raise WavespeedMcpError(f"Unexpected error processing image: {str(e)}") 201 | 202 | 203 | def process_image_input(image_input: str) -> str: 204 | """Process different types of image input and return a format suitable for API calls. 205 | 206 | Args: 207 | image_input: Can be a URL, base64 string, or local file path 208 | 209 | Returns: 210 | Processed image string (URL or base64 with data URI prefix) 211 | 212 | Raises: 213 | WavespeedMcpError: If the image cannot be processed 214 | """ 215 | import base64 216 | import re 217 | from pathlib import Path 218 | 219 | # Check if input is empty 220 | if not image_input: 221 | return "" 222 | 223 | # Check if input is already a URL 224 | if image_input.startswith(("http://", "https://", "ftp://")): 225 | return image_input 226 | 227 | # Check if input is already a base64 string with data URI prefix 228 | if image_input.startswith("data:image/"): 229 | return image_input 230 | 231 | # Check if input is a base64 string without prefix 232 | # Base64 strings are typically long and contain only certain characters 233 | if len(image_input) > 100 and re.match(r"^[A-Za-z0-9+/]+={0,2}$", image_input): 234 | # Add data URI prefix 235 | return f"data:image/jpeg;base64,{image_input}" 236 | 237 | # Check if input is a local file path 238 | path = Path(image_input) 239 | if path.exists() and path.is_file(): 240 | try: 241 | # Read file and convert to base64 242 | with open(path, "rb") as f: 243 | file_data = f.read() 244 | 245 | # Determine MIME type based on file extension 246 | mime_type = "image/jpeg" # Default 247 | if path.suffix.lower() in [".png"]: 248 | mime_type = "image/png" 249 | elif path.suffix.lower() in [".gif"]: 250 | mime_type = "image/gif" 251 | elif path.suffix.lower() in [".webp"]: 252 | mime_type = "image/webp" 253 | 254 | # Convert to base64 and add data URI prefix 255 | base64_data = base64.b64encode(file_data).decode("utf-8") 256 | return f"data:{mime_type};base64,{base64_data}" 257 | except Exception as e: 258 | raise WavespeedMcpError(f"Failed to process local image file: {str(e)}") 259 | 260 | # If we get here, the input format is unknown 261 | raise WavespeedMcpError(f"Unrecognized image input format: {image_input[:50]}...") 262 | 263 | 264 | def is_english_text(text: str) -> bool: 265 | """ 266 | Check if a text is primarily in English. 267 | 268 | Args: 269 | text: The text to check 270 | 271 | Returns: 272 | True if the text is primarily in English, False otherwise 273 | """ 274 | # Simple heuristic: Check if most characters are ASCII 275 | # This is a basic check and can be improved with more sophisticated language detection 276 | if not text: 277 | return True 278 | 279 | # Count ASCII characters (English text is mostly ASCII) 280 | ascii_count = sum(1 for char in text if ord(char) < 128) 281 | # If more than 80% of characters are ASCII, consider it English 282 | return ascii_count / len(text) > 0.8 283 | 284 | 285 | def validate_loras( 286 | loras: Optional[List[Dict[str, Union[str, float]]]] 287 | ) -> List[Dict[str, Union[str, float]]]: 288 | """Validate lora configuration. 289 | 290 | Args: 291 | loras: List of lora configurations 292 | 293 | Returns: 294 | Validated list of lora configurations 295 | 296 | Raises: 297 | WavespeedMcpError: If loras are invalid 298 | """ 299 | if not loras: 300 | return [] 301 | 302 | for lora in loras: 303 | if not isinstance(lora, dict): 304 | raise WavespeedMcpError(f"Invalid lora configuration: {lora}") 305 | 306 | if "path" not in lora: 307 | raise WavespeedMcpError(f"Missing 'path' in lora configuration: {lora}") 308 | 309 | if "scale" not in lora: 310 | lora["scale"] = 1.0 311 | 312 | return loras 313 | -------------------------------------------------------------------------------- /wavespeed_mcp/server.py: -------------------------------------------------------------------------------- 1 | """ 2 | WaveSpeed MCP Server 3 | 4 | This server connects to WaveSpeed AI API endpoints which may involve costs. 5 | Any tool that makes an API call is clearly marked with a cost warning. 6 | 7 | Note: Always ensure you have proper API credentials before using these tools. 8 | """ 9 | 10 | import os 11 | import requests 12 | import time 13 | import json 14 | import logging 15 | import uuid 16 | from typing import Dict, List, Optional, Union 17 | from pydantic import BaseModel 18 | from dotenv import load_dotenv 19 | from mcp.server.fastmcp import FastMCP 20 | from mcp.types import TextContent 21 | 22 | from wavespeed_mcp.utils import ( 23 | build_output_path, 24 | build_output_file, 25 | validate_loras, 26 | get_image_as_base64, 27 | process_image_input, 28 | is_english_text, 29 | ) 30 | from wavespeed_mcp.const import ( 31 | DEFAULT_IMAGE_SIZE, 32 | DEFAULT_NUM_INFERENCE_STEPS, 33 | DEFAULT_GUIDANCE_SCALE, 34 | DEFAULT_NUM_IMAGES, 35 | DEFAULT_SEED, 36 | DEFAULT_IMAGE_LORA, 37 | ENV_WAVESPEED_API_KEY, 38 | ENV_WAVESPEED_API_HOST, 39 | ENV_WAVESPEED_MCP_BASE_PATH, 40 | ENV_RESOURCE_MODE, 41 | RESOURCE_MODE_URL, 42 | RESOURCE_MODE_BASE64, 43 | DEFAULT_LOG_LEVEL, 44 | ENV_FASTMCP_LOG_LEVEL, 45 | ENV_WAVESPEED_LOG_FILE, 46 | DEFAULT_REQUEST_TIMEOUT, 47 | ENV_WAVESPEED_REQUEST_TIMEOUT, 48 | DEFAULT_WAIT_RESULT_TIMEOUT, 49 | ENV_WAVESPEED_WAIT_RESULT_TIMEOUT, 50 | API_VERSION, 51 | API_BASE_PATH, 52 | API_IMAGE_ENDPOINT, 53 | API_VIDEO_ENDPOINT, 54 | API_IMAGE_TO_IMAGE_ENDPOINT, 55 | ENV_API_TEXT_TO_IMAGE_ENDPOINT, 56 | ENV_API_IMAGE_TO_IMAGE_ENDPOINT, 57 | ENV_API_VIDEO_ENDPOINT, 58 | ) 59 | from wavespeed_mcp.exceptions import ( 60 | WavespeedRequestError, 61 | WavespeedAuthError, 62 | WavespeedTimeoutError, 63 | ) 64 | from wavespeed_mcp.client import WavespeedAPIClient 65 | 66 | # Load environment variables 67 | load_dotenv() 68 | 69 | # Configure logging 70 | log_file = os.getenv(ENV_WAVESPEED_LOG_FILE) 71 | log_level = os.getenv(ENV_FASTMCP_LOG_LEVEL, DEFAULT_LOG_LEVEL) 72 | log_format = "%(asctime)s - wavespeed-mcp - %(levelname)s - %(message)s" 73 | 74 | if log_file: 75 | # Configure file logging 76 | import logging.handlers 77 | 78 | # Create log directory if it doesn't exist 79 | log_dir = os.path.dirname(log_file) 80 | if log_dir and not os.path.exists(log_dir): 81 | os.makedirs(log_dir, exist_ok=True) 82 | 83 | # Configure rotating file handler 84 | handler = logging.handlers.RotatingFileHandler( 85 | log_file, maxBytes=10 * 1024 * 1024, backupCount=5 # 10MB max, 5 backups 86 | ) 87 | handler.setFormatter(logging.Formatter(log_format)) 88 | 89 | # Configure root logger 90 | logging.basicConfig( 91 | level=log_level, 92 | handlers=[handler], 93 | format=log_format, 94 | ) 95 | else: 96 | # Default console logging 97 | logging.basicConfig( 98 | level=log_level, 99 | format=log_format, 100 | ) 101 | 102 | logger = logging.getLogger("wavespeed-mcp") 103 | 104 | # Get configuration from environment variables 105 | api_key = os.getenv(ENV_WAVESPEED_API_KEY) 106 | api_host = os.getenv(ENV_WAVESPEED_API_HOST, "https://api.wavespeed.ai") 107 | base_path = os.getenv(ENV_WAVESPEED_MCP_BASE_PATH) or "~/Desktop" 108 | resource_mode = os.getenv(ENV_RESOURCE_MODE, RESOURCE_MODE_URL) 109 | # Per-request HTTP timeout 110 | request_timeout = int(os.getenv(ENV_WAVESPEED_REQUEST_TIMEOUT, DEFAULT_REQUEST_TIMEOUT)) 111 | # Total wait timeout for polling results 112 | wait_result_timeout = int( 113 | os.getenv(ENV_WAVESPEED_WAIT_RESULT_TIMEOUT, DEFAULT_WAIT_RESULT_TIMEOUT) 114 | ) 115 | 116 | # Validate required environment variables 117 | if not api_key: 118 | raise ValueError(f"{ENV_WAVESPEED_API_KEY} environment variable is required") 119 | 120 | # Initialize MCP server and API client 121 | mcp = FastMCP( 122 | "WaveSpeed", log_level=os.getenv(ENV_FASTMCP_LOG_LEVEL, DEFAULT_LOG_LEVEL) 123 | ) 124 | api_client = WavespeedAPIClient(api_key, f"{api_host}{API_BASE_PATH}/{API_VERSION}") 125 | 126 | 127 | class FileInfo(BaseModel): 128 | """Information about a local file.""" 129 | 130 | path: str 131 | index: int 132 | 133 | 134 | class Base64Info(BaseModel): 135 | """Information about a base64 encoded resource.""" 136 | 137 | data: str 138 | mime_type: str 139 | index: int 140 | 141 | 142 | class WaveSpeedResult(BaseModel): 143 | """Unified model for WaveSpeed generation results.""" 144 | 145 | status: str = "success" 146 | urls: List[str] = [] 147 | base64: List[Base64Info] = [] 148 | local_files: List[FileInfo] = [] 149 | error: Optional[str] = None 150 | processing_time: float = 0.0 151 | model: Optional[str] = None 152 | 153 | def to_json(self) -> str: 154 | """Convert the result to a JSON string.""" 155 | return json.dumps(self.model_dump(), indent=2) 156 | 157 | 158 | def _process_wavespeed_request( 159 | api_endpoint: str, 160 | payload: dict, 161 | output_directory: Optional[str], 162 | prompt: str, 163 | resource_type: str = "image", # "image" or "video" 164 | operation_name: str = "Generation", 165 | request_id: str = None, 166 | ) -> TextContent: 167 | """Process a WaveSpeed API request and handle the response. 168 | 169 | This is a common function to handle API requests, polling for results, 170 | and processing the output based on the resource mode. 171 | 172 | Args: 173 | api_endpoint: The API endpoint to call 174 | payload: The request payload 175 | output_directory: Directory to save generated files 176 | prompt: The prompt used for generation 177 | resource_type: Type of resource being generated ("image" or "video") 178 | operation_name: Name of the operation for logging 179 | 180 | Returns: 181 | TextContent with the result JSON 182 | """ 183 | 184 | begin_time = time.time() 185 | 186 | # Log API request details 187 | logger.info(f"[{request_id}] Making {operation_name} API request to {api_endpoint}") 188 | logger.info(f"[{request_id}] Request payload: {json.dumps(payload, indent=2)}") 189 | 190 | try: 191 | # Make API request 192 | response_data = api_client.post(api_endpoint, json=payload) 193 | wavespeed_request_id = response_data.get("data", {}).get("id") 194 | 195 | if not wavespeed_request_id: 196 | logger.error( 197 | f"[{request_id}] Failed to get WaveSpeed request ID from response" 198 | ) 199 | error_result = WaveSpeedResult( 200 | status="error", 201 | error="Failed to get request ID from response. Please try again.", 202 | ) 203 | return TextContent(type="text", text=error_result.to_json()) 204 | 205 | logger.info( 206 | f"[{request_id}] {operation_name} request submitted with WaveSpeed ID: {wavespeed_request_id}" 207 | ) 208 | 209 | # Poll for results using server-level wait_result_timeout 210 | result = api_client.poll_result( 211 | wavespeed_request_id, 212 | request_id=request_id, 213 | total_timeout=wait_result_timeout, 214 | ) 215 | outputs = result.get("outputs", []) 216 | 217 | if not outputs: 218 | error_result = WaveSpeedResult( 219 | status="error", 220 | error=f"No {resource_type} outputs received. Please try again.", 221 | ) 222 | return TextContent(type="text", text=error_result.to_json()) 223 | 224 | end = time.time() 225 | processing_time = end - begin_time 226 | 227 | model = result.get("model", "") 228 | 229 | logger.info( 230 | f"[{request_id}] {operation_name} completed in {processing_time:.2f} seconds" 231 | ) 232 | 233 | # Prepare result 234 | result = WaveSpeedResult( 235 | urls=outputs, processing_time=processing_time, model=model 236 | ) 237 | 238 | # Handle different resource modes 239 | if resource_mode == RESOURCE_MODE_URL: 240 | # Only return URLs 241 | pass 242 | elif resource_mode == RESOURCE_MODE_BASE64: 243 | # Get base64 encoding 244 | if resource_type == "video": 245 | # For video, usually just one is returned 246 | video_url = outputs[0] 247 | try: 248 | response = requests.get(video_url, timeout=request_timeout) 249 | response.raise_for_status() 250 | 251 | # Convert to base64 252 | import base64 253 | 254 | base64_data = base64.b64encode(response.content).decode("utf-8") 255 | 256 | result.base64.append( 257 | Base64Info(data=base64_data, mime_type="video/mp4", index=0) 258 | ) 259 | 260 | logger.info(f"Successfully encoded {resource_type} to base64") 261 | except Exception as e: 262 | logger.error(f"Failed to encode {resource_type}: {str(e)}") 263 | else: 264 | # For images, handle multiple outputs 265 | for i, url in enumerate(outputs): 266 | try: 267 | # Get base64 encoding and MIME type 268 | base64_data, mime_type = get_image_as_base64(url) 269 | result.base64.append( 270 | Base64Info(data=base64_data, mime_type=mime_type, index=i) 271 | ) 272 | logger.info( 273 | f"Successfully encoded {resource_type} {i+1}/{len(outputs)} to base64" 274 | ) 275 | except Exception as e: 276 | logger.error( 277 | f"Failed to encode {resource_type} {i+1}: {str(e)}" 278 | ) 279 | else: 280 | # Save to local file 281 | output_path = build_output_path(output_directory, base_path) 282 | output_path.mkdir(parents=True, exist_ok=True) 283 | 284 | if resource_type == "video": 285 | # For video, usually just one is returned 286 | video_url = outputs[0] 287 | try: 288 | filename = build_output_file( 289 | resource_type, prompt, output_path, "mp4" 290 | ) 291 | 292 | response = requests.get( 293 | video_url, stream=True, timeout=request_timeout 294 | ) 295 | response.raise_for_status() 296 | 297 | with open(filename, "wb") as f: 298 | for chunk in response.iter_content(chunk_size=8192): 299 | f.write(chunk) 300 | 301 | result.local_files.append(FileInfo(path=str(filename), index=0)) 302 | logger.info(f"Successfully saved {resource_type} to {filename}") 303 | except Exception as e: 304 | logger.error(f"Failed to save {resource_type}: {str(e)}") 305 | else: 306 | # For images, handle multiple outputs 307 | for i, url in enumerate(outputs): 308 | try: 309 | output_file_name = build_output_file( 310 | resource_type, f"{i}_{prompt}", output_path, "jpeg" 311 | ) 312 | 313 | response = requests.get(url, timeout=request_timeout) 314 | response.raise_for_status() 315 | 316 | with open(output_file_name, "wb") as f: 317 | f.write(response.content) 318 | 319 | result.local_files.append( 320 | FileInfo(path=str(output_file_name), index=i) 321 | ) 322 | logger.info( 323 | f"Successfully saved {resource_type} {i+1}/{len(outputs)} to {output_file_name}" 324 | ) 325 | except Exception as e: 326 | logger.error(f"Failed to save {resource_type} {i+1}: {str(e)}") 327 | 328 | # Return unified JSON structure 329 | return TextContent(type="text", text=result.to_json()) 330 | 331 | except (WavespeedAuthError, WavespeedRequestError, WavespeedTimeoutError) as e: 332 | logger.error(f"[{request_id}] {operation_name} failed: {str(e)}") 333 | error_result = WaveSpeedResult( 334 | status="error", error=f"Failed to generate {resource_type}: {str(e)}" 335 | ) 336 | return TextContent(type="text", text=error_result.to_json()) 337 | except Exception as e: 338 | logger.exception( 339 | f"[{request_id}] Unexpected error during {operation_name.lower()}: {str(e)}" 340 | ) 341 | error_result = WaveSpeedResult( 342 | status="error", error=f"An unexpected error occurred: {str(e)}" 343 | ) 344 | return TextContent(type="text", text=error_result.to_json()) 345 | 346 | 347 | def get_models(model): 348 | if model == "" or model is None: 349 | model = os.getenv(ENV_API_TEXT_TO_IMAGE_ENDPOINT, API_IMAGE_ENDPOINT) 350 | 351 | if not model.startswith("/"): 352 | model = "/" + model 353 | 354 | return model 355 | 356 | 357 | def get_image_models(model): 358 | if model == "" or model is None: 359 | model = os.getenv(ENV_API_IMAGE_TO_IMAGE_ENDPOINT, API_IMAGE_TO_IMAGE_ENDPOINT) 360 | 361 | if not model.startswith("/"): 362 | model = "/" + model 363 | 364 | return model 365 | 366 | 367 | def get_video_models(model): 368 | if model == "" or model is None: 369 | model = os.getenv(ENV_API_VIDEO_ENDPOINT, API_VIDEO_ENDPOINT) 370 | 371 | if not model.startswith("/"): 372 | model = "/" + model 373 | 374 | return model 375 | 376 | 377 | @mcp.tool( 378 | description="""Generate an image from text prompt using WaveSpeed AI. 379 | 380 | Args: 381 | prompt (str): Required. Text description of the image to generate. MUST BE IN ENGLISH. Non-English prompts will be rejected or result in poor quality outputs. 382 | model (str, optional): Model to use for image generation. 383 | loras (list, optional): List of LoRA models to use, each with a path and scale. Format: [{"path": "model_path", "scale": weight_value}]. Default model used if not provided. 384 | size (str, optional): Size of the output image in format "width*height", e.g., "512*512". Default: 1024*1024. 385 | num_inference_steps (int, optional): Number of denoising steps. Higher values improve quality but increase generation time. Default: 30. 386 | guidance_scale (float, optional): Guidance scale for text adherence. Controls how closely the image matches the text description. Default: 7.5. 387 | num_images (int, optional): Number of images to generate. Default: 1. 388 | seed (int, optional): Random seed for reproducible results. Set to -1 for random. Default: -1. 389 | enable_safety_checker (bool, optional): Whether to enable safety filtering. Default: True. 390 | output_directory (str, optional): Directory to save the generated images. Uses a temporary directory if not provided. 391 | request_id (str, optional): Request correlation ID for tracing the entire request chain. Strongly recommended to provide a unique ID (e.g., UUID) to correlate logs across the request lifecycle. 392 | 393 | Returns: 394 | WaveSpeedResult object with the result of the image generation, containing: 395 | - status: "success" or "error" 396 | - urls: List of image URLs if successful 397 | - base64: List of base64 encoded images if resource_mode is set to base64 398 | - local_files: List of local file paths if resource_mode is set to local 399 | - error: Error message if status is "error" 400 | - processing_time: Time taken to generate the image(s) 401 | 402 | Examples: 403 | Basic usage: text_to_image(prompt="A golden retriever running on grass") 404 | Advanced usage: text_to_image( 405 | prompt="A golden retriever running on grass", 406 | size="1024*1024", 407 | num_inference_steps=50, 408 | seed=42 409 | ) 410 | 411 | Note: 412 | For optimal results, always provide prompts in English, regardless of your interface language. 413 | Non-English prompts may result in lower quality or unexpected images. 414 | """ 415 | ) 416 | def text_to_image( 417 | prompt: str, 418 | model: Optional[str] = None, 419 | loras: Optional[List[Dict[str, Union[str, float]]]] = None, 420 | size: str = DEFAULT_IMAGE_SIZE, 421 | num_inference_steps: int = DEFAULT_NUM_INFERENCE_STEPS, 422 | guidance_scale: float = DEFAULT_GUIDANCE_SCALE, 423 | num_images: int = DEFAULT_NUM_IMAGES, 424 | seed: int = DEFAULT_SEED, 425 | enable_safety_checker: bool = True, 426 | output_directory: str = None, 427 | request_id: str = None, 428 | ): 429 | """Generate an image from text prompt using WaveSpeed AI.""" 430 | 431 | # Generate unique request ID for tracking 432 | if not request_id: 433 | request_id = str(uuid.uuid4())[:8] 434 | logger.info( 435 | f"[{request_id}] MCP text_to_image request - prompt: '{prompt[:100]}{'...' if len(prompt) > 100 else ''}', model: {model}, size: {size}, num_images: {num_images}" 436 | ) 437 | 438 | if not prompt: 439 | error_result = WaveSpeedResult( 440 | status="error", 441 | error="Prompt is required for image generation. Please provide an English prompt for optimal results.", 442 | ) 443 | return TextContent(type="text", text=error_result.to_json()) 444 | 445 | # Check if prompt is in English 446 | if not is_english_text(prompt): 447 | error_result = WaveSpeedResult( 448 | status="error", 449 | error="Prompt must be in English. Please provide an English prompt for optimal results.", 450 | ) 451 | return TextContent(type="text", text=error_result.to_json()) 452 | 453 | # Validate and set default loras if not provided 454 | # if not loras: 455 | # loras = [DEFAULT_IMAGE_LORA] 456 | # else: 457 | if loras: 458 | loras = validate_loras(loras) 459 | else: 460 | loras = [] 461 | 462 | # Prepare API payload 463 | payload = { 464 | "prompt": prompt, 465 | "loras": loras, 466 | "size": size, 467 | "num_inference_steps": num_inference_steps, 468 | "guidance_scale": guidance_scale, 469 | "num_images": num_images, 470 | "max_images": num_images, 471 | "seed": seed, 472 | "enable_base64_output": False, # 使用URL，后续自己转换为base64 473 | "enable_safety_checker": enable_safety_checker, 474 | } 475 | 476 | return _process_wavespeed_request( 477 | api_endpoint=get_models(model), 478 | payload=payload, 479 | output_directory=output_directory, 480 | prompt=prompt, 481 | resource_type="image", 482 | operation_name="Image generation", 483 | request_id=request_id, 484 | ) 485 | 486 | 487 | @mcp.tool( 488 | description="""Generate an image from an existing image using WaveSpeed AI. 489 | 490 | Args: 491 | image (str): Required. URL, base64 string, or local file path of the input image to modify. 492 | images (List[str]): Required. List of URLs to images to modify. 493 | prompt (str): Required. Text description of the desired modifications. MUST BE IN ENGLISH. Non-English prompts will be rejected or result in poor quality outputs. 494 | model (str, optional): Model to use for image generation. 495 | guidance_scale (float, optional): Guidance scale for text adherence. Controls how closely the output follows the prompt. Range: [1.0-10.0]. Default: 3.5. 496 | enable_safety_checker (bool, optional): Whether to enable safety filtering. Default: True. 497 | output_directory (str, optional): Directory to save the generated images. Uses a temporary directory if not provided. 498 | request_id (str, optional): Request correlation ID for tracing the entire request chain. Strongly recommended to provide a unique ID (e.g., UUID) to correlate logs across the request lifecycle. 499 | 500 | Returns: 501 | WaveSpeedResult object with the result of the image generation, containing: 502 | - status: "success" or "error" 503 | - urls: List of image URLs if successful 504 | - base64: List of base64 encoded images if resource_mode is set to base64 505 | - local_files: List of local file paths if resource_mode is set to local 506 | - error: Error message if status is "error" 507 | - processing_time: Time taken to generate the image(s) 508 | 509 | Examples: 510 | Single image: image_to_image(image="https://example.com/image.jpg", images=[], prompt="Make it look like winter") 511 | Multiple images: image_to_image(image="", images=["https://example.com/img1.jpg", "https://example.com/img2.jpg"], prompt="Convert to oil painting style") 512 | Both parameters: image_to_image(image="https://example.com/main.jpg", images=["https://example.com/ref1.jpg"], prompt="Apply style transfer") 513 | 514 | Note: 515 | For optimal results, always provide prompts in English, regardless of your interface language. 516 | Non-English prompts may result in lower quality or unexpected images. 517 | """ 518 | ) 519 | def image_to_image( 520 | image: str, 521 | images: List[str], 522 | prompt: str, 523 | model: Optional[str] = None, 524 | guidance_scale: float = 3.5, 525 | enable_safety_checker: bool = True, 526 | output_directory: str = None, 527 | request_id: str = None, 528 | ): 529 | """Generate an image from an existing image using WaveSpeed AI.""" 530 | 531 | # Generate unique request ID for tracking 532 | if not request_id: 533 | request_id = str(uuid.uuid4())[:8] 534 | logger.info( 535 | f"[{request_id}] MCP image_to_image request - prompt: '{prompt[:100]}{'...' if len(prompt) > 100 else ''}', model: {model}, image: {image}, images_count: {len(images)}" 536 | ) 537 | 538 | if not image and not images: 539 | error_result = WaveSpeedResult( 540 | status="error", 541 | error="Input image(s) required for image-to-image generation. Provide either 'image' or 'images' parameter.", 542 | ) 543 | return TextContent(type="text", text=error_result.to_json()) 544 | 545 | if not prompt: 546 | error_result = WaveSpeedResult( 547 | status="error", 548 | error="Prompt is required for image-to-image generation. Please provide an English prompt for optimal results.", 549 | ) 550 | return TextContent(type="text", text=error_result.to_json()) 551 | 552 | # Check if prompt is in English 553 | if not is_english_text(prompt): 554 | error_result = WaveSpeedResult( 555 | status="error", 556 | error="Prompt must be in English. Please provide an English prompt for optimal results.", 557 | ) 558 | return TextContent(type="text", text=error_result.to_json()) 559 | 560 | # handle image input(s) 561 | payload = { 562 | "prompt": prompt, 563 | "guidance_scale": guidance_scale, 564 | "enable_safety_checker": enable_safety_checker, 565 | } 566 | 567 | try: 568 | # Process single image if provided 569 | if image: 570 | processed_image = process_image_input(image) 571 | payload["image"] = processed_image 572 | logger.info("Successfully processed single input image") 573 | 574 | # Process multiple images if provided 575 | if images: 576 | processed_images = [] 577 | for img in images: 578 | processed_img = process_image_input(img) 579 | processed_images.append(processed_img) 580 | payload["images"] = processed_images 581 | logger.info(f"Successfully processed {len(processed_images)} input images") 582 | 583 | if not image: 584 | image = images[0] 585 | payload["image"] = image 586 | 587 | if not images: 588 | images = [image] 589 | payload["images"] = images 590 | 591 | except Exception as e: 592 | logger.error(f"Failed to process input image(s): {str(e)}") 593 | error_result = WaveSpeedResult( 594 | status="error", error=f"Failed to process input image(s): {str(e)}" 595 | ) 596 | return TextContent(type="text", text=error_result.to_json()) 597 | 598 | return _process_wavespeed_request( 599 | api_endpoint=get_image_models(model), 600 | payload=payload, 601 | output_directory=output_directory, 602 | prompt=prompt, 603 | resource_type="image", 604 | operation_name="Image-to-image generation", 605 | request_id=request_id, 606 | ) 607 | 608 | 609 | @mcp.tool( 610 | description="""Generate a video using WaveSpeed AI. 611 | 612 | Args: 613 | image (str): Required. URL, base64 string, or local file path of the input image to animate. 614 | prompt (str): Required. Text description of the video to generate. MUST BE IN ENGLISH. Non-English prompts will be rejected or result in poor quality outputs. 615 | model (str, optional): Model to use for video generation. 616 | negative_prompt (str, optional): Text description of what to avoid in the video. Default: "". 617 | loras (list, optional): List of LoRA models to use, each with a path and scale. Format: [{"path": "model_path", "scale": weight_value}]. Default: []. 618 | size (str, optional): Size of the output video in format "width*height". Default: "832*480". 619 | num_inference_steps (int, optional): Number of denoising steps. Higher values improve quality but increase generation time. Default: 30. 620 | duration (int, optional): Duration of the video in seconds. Must be either 5 or 10. Default: 5. 621 | guidance_scale (float, optional): Guidance scale for text adherence. Controls how closely the video matches the text description. Default: 5. 622 | flow_shift (int, optional): Shift of the flow in the video. Affects motion intensity. Default: 3. 623 | seed (int, optional): Random seed for reproducible results. Set to -1 for random. Default: -1. 624 | enable_safety_checker (bool, optional): Whether to enable safety filtering. Default: True. 625 | output_directory (str, optional): Directory to save the generated video. Uses a temporary directory if not provided. 626 | request_id (str, optional): Request correlation ID for tracing the entire request chain. Strongly recommended to provide a unique ID (e.g., UUID) to correlate logs across the request lifecycle. 627 | 628 | Returns: 629 | WaveSpeedResult object with the result of the video generation, containing: 630 | - status: "success" or "error" 631 | - urls: List of video URLs if successful 632 | - base64: List of base64 encoded videos if resource_mode is set to base64 633 | - local_files: List of local file paths if resource_mode is set to local 634 | - error: Error message if status is "error" 635 | - processing_time: Time taken to generate the video(s) 636 | 637 | Examples: 638 | Basic usage: generate_video(image="https://example.com/image.jpg", prompt="The dog running through a forest") 639 | Advanced usage: generate_video( 640 | image="/path/to/local/image.jpg", 641 | prompt="The dog running through a forest", 642 | duration=10, 643 | negative_prompt="blurry, low quality" 644 | ) 645 | 646 | Note: 647 | IMPORTANT: Prompts MUST be in English. The system only processes English prompts properly. 648 | Non-English prompts will be rejected or produce low-quality results. If user input is not in English, 649 | you MUST translate it to English before passing to this tool. 650 | """ 651 | ) 652 | def generate_video( 653 | image: str, 654 | prompt: str, 655 | model: Optional[str] = None, 656 | negative_prompt: str = "", 657 | loras: Optional[List[Dict[str, Union[str, float]]]] = None, 658 | size: str = "832*480", 659 | num_inference_steps: int = 30, 660 | duration: int = 5, 661 | guidance_scale: float = 5, 662 | flow_shift: int = 3, 663 | seed: int = -1, 664 | enable_safety_checker: bool = True, 665 | output_directory: str = None, 666 | request_id: str = None, 667 | ): 668 | """Generate a video using WaveSpeed AI.""" 669 | 670 | # Generate unique request ID for tracking 671 | if not request_id: 672 | request_id = str(uuid.uuid4())[:8] 673 | logger.info( 674 | f"[{request_id}] MCP generate_video request - prompt: '{prompt[:100]}{'...' if len(prompt) > 100 else ''}', model: {model}, image: {image}, duration: {duration}s" 675 | ) 676 | 677 | if not image: 678 | # raise WavespeedRequestError("Input image is required for video generation") 679 | error_result = WaveSpeedResult( 680 | status="error", 681 | error="Input image is required for video generation. Can use generate_image tool to generate an image first.", 682 | ) 683 | return TextContent(type="text", text=error_result.to_json()) 684 | 685 | if not prompt: 686 | # raise WavespeedRequestError("Prompt is required for video generation") 687 | error_result = WaveSpeedResult( 688 | status="error", 689 | error="Prompt is required for video generation. Please provide an English prompt for optimal results.", 690 | ) 691 | return TextContent(type="text", text=error_result.to_json()) 692 | 693 | # Check if prompt is in English 694 | if not is_english_text(prompt): 695 | error_result = WaveSpeedResult( 696 | status="error", 697 | error="Prompt must be in English. Please provide an English prompt for optimal results.", 698 | ) 699 | return TextContent(type="text", text=error_result.to_json()) 700 | 701 | # Validate and set default loras if not provided 702 | if not loras: 703 | loras = [] 704 | else: 705 | loras = validate_loras(loras) 706 | 707 | if duration not in [5, 10]: 708 | error_result = WaveSpeedResult( 709 | status="error", 710 | error="Duration must be 5 or 10 seconds. Please set it to 5 or 10.", 711 | ) 712 | return TextContent(type="text", text=error_result.to_json()) 713 | 714 | # handle image input 715 | try: 716 | processed_image = process_image_input(image) 717 | logger.info("Successfully processed input image") 718 | except Exception as e: 719 | logger.error(f"Failed to process input image: {str(e)}") 720 | error_result = WaveSpeedResult( 721 | status="error", error=f"Failed to process input image: {str(e)}" 722 | ) 723 | return TextContent(type="text", text=error_result.to_json()) 724 | 725 | # Prepare API payload 726 | payload = { 727 | "image": processed_image, 728 | "prompt": prompt, 729 | "negative_prompt": negative_prompt, 730 | "loras": loras, 731 | "size": size, 732 | "num_inference_steps": num_inference_steps, 733 | "duration": duration, 734 | "guidance_scale": guidance_scale, 735 | "flow_shift": flow_shift, 736 | "seed": seed, 737 | "enable_safety_checker": enable_safety_checker, 738 | } 739 | 740 | return _process_wavespeed_request( 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