[{"_1":2,"_1422":-5,"_1423":-5},"loaderData",{"_3":4,"_20":21,"_22":23,"_920":921},"root",{"_5":6,"_7":8},"toasts",[],"visitor",{"_9":10,"_11":12},"viewportWidth",1280,"visitorSession",{"_13":14,"_16":-5,"_17":18,"_19":-5},"attributes",[15],"BOT","membership","uid","6431mA9mNW","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_24":25,"_66":-5,"_67":26,"_27":68,"_839":840,"_918":919},"availableLocales",[26,38,45,52,59],{"_27":28,"_31":35,"_36":37},"mcpServer",{"_29":30,"_31":32,"_33":34},"description","A protocol bridge that connects AI systems to Taiga project management platform, enabling AI tools to create and manage projects, epics, user stories, tasks, issues, and sprints.","name","Taiga MCP Bridge","readme","

\n\n \n\n

\n\n# Taiga MCP Bridge\n\n\n[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)\n\n## Overview\n\nThe Taiga MCP Bridge is a powerful integration layer that connects [Taiga](https://taiga.io/) project management platform with the Model Context Protocol (MCP), enabling AI tools and workflows to interact seamlessly with Taiga's resources.\n\nThis bridge provides a comprehensive set of tools and resources for AI agents to:\n- Create and manage projects, epics, user stories, tasks, and issues in Taiga\n- Track sprints and milestones\n- Assign and update work items\n- Query detailed information about project artifacts\n- Manage project members and permissions\n\nBy using the MCP standard, this bridge allows AI systems to maintain contextual awareness about project state and perform complex project management tasks programmatically.\n\n## Features\n\n### Comprehensive Resource Support\n\nThe bridge supports the following Taiga resources with complete CRUD operations:\n\n- **Projects**: Create, update, and manage project settings and metadata\n- **Epics**: Manage large features that span multiple sprints\n- **User Stories**: Handle detailed requirements and acceptance criteria\n- **Tasks**: Track smaller units of work within user stories\n- **Issues**: Manage bugs, questions, and enhancement requests\n- **Sprints (Milestones)**: Plan and track work in time-boxed intervals\n\n## Installation\n\nThis project uses [uv](https://github.com/astral-sh/uv) for fast, reliable Python package management.\n\n### Prerequisites\n\n- Python 3.10 or higher\n- uv package manager\n\n### Basic Installation\n\n```bash\n# Clone the repository\ngit clone https://github.com/your-org/pyTaigaMCP.git\ncd pyTaigaMCP\n\n# Install dependencies\n./install.sh\n```\n\n### Development Installation\n\nFor development (includes testing and code quality tools):\n\n```bash\n./install.sh --dev\n```\n\n### Manual Installation\n\nIf you prefer to install manually:\n\n```bash\n# Production dependencies only\nuv pip install -e .\n\n# With development dependencies\nuv pip install -e \".[dev]\"\n```\n\n## Configuration\n\nThe bridge can be configured through environment variables or a `.env` file:\n\n| Environment Variable | Description | Default |\n| --- | --- | --- |\n| `TAIGA_API_URL` | Base URL for the Taiga API | http://localhost:9000 |\n| `SESSION_EXPIRY` | Session expiration time in seconds | 28800 (8 hours) |\n| `TAIGA_TRANSPORT` | Transport mode (stdio or sse) | stdio |\n| `REQUEST_TIMEOUT` | API request timeout in seconds | 30 |\n| `MAX_CONNECTIONS` | Maximum number of HTTP connections | 10 |\n| `MAX_KEEPALIVE_CONNECTIONS` | Max keepalive connections | 5 |\n| `RATE_LIMIT_REQUESTS` | Max requests per minute | 100 |\n| `LOG_LEVEL` | Logging level | INFO |\n| `LOG_FILE` | Path to log file | taiga_mcp.log |\n\nCreate a `.env` file in the project root to set these values:\n\n```\nTAIGA_API_URL=https://api.taiga.io/api/v1/\nTAIGA_TRANSPORT=sse\nLOG_LEVEL=DEBUG\n```\n\n## Usage\n\n### With stdio mode\n\nPaste the following json in your Claude App's or Cursor's mcp settings section:\n\n```json\n{\n \"mcpServers\": {\n \"taigaApi\": {\n \"command\": \"uv\",\n \"args\": [\n \"--directory\",\n \"\",\n \"run\",\n \"src/server.py\"\n ],\n \"env\": {\n \"TAIGA_TRANSPORT\": \"\", \n \"TAIGA_API_URL\": \"\",\n \"TAIGA_PASSWORD\": \"\"\n }\n }\n}\n```\n\n### Running the Bridge\n\nStart the MCP server with:\n\n```bash\n# Default stdio transport\n./run.sh\n\n# For SSE transport\n./run.sh --sse\n```\n\nOr manually:\n\n```bash\n# For stdio transport (default)\nuv run python src/server.py\n\n# For SSE transport\nuv run python src/server.py --sse\n```\n\n### Transport Modes\n\nThe server supports two transport modes:\n\n1. **stdio (Standard Input/Output)** - Default mode for terminal-based clients\n2. **SSE (Server-Sent Events)** - Web-based transport with server push capabilities\n\nYou can set the transport mode in several ways:\n- Using the `--sse` flag with run.sh or server.py (default is stdio)\n- Setting the `TAIGA_TRANSPORT` environment variable \n- Adding `TAIGA_TRANSPORT=sse` to your `.env` file\n\n### Authentication Flow\n\nThis MCP bridge uses a session-based authentication model:\n\n1. **Login**: Clients must first authenticate using the `login` tool:\n ```python\n session = client.call_tool(\"login\", {\n \"username\": \"your_taiga_username\",\n \"password\": \"your_taiga_password\",\n \"host\": \"https://api.taiga.io\" # Optional\n })\n # Save the session_id from the response\n session_id = session[\"session_id\"]\n ```\n\n2. **Using Tools and Resources**: Include the `session_id` in every API call:\n ```python\n # For resources, include session_id in the URI\n projects = client.get_resource(f\"taiga://projects?session_id={session_id}\")\n \n # For project-specific resources\n epics = client.get_resource(f\"taiga://projects/123/epics?session_id={session_id}\")\n \n # For tools, include session_id as a parameter\n new_project = client.call_tool(\"create_project\", {\n \"session_id\": session_id,\n \"name\": \"New Project\",\n \"description\": \"Description\"\n })\n ```\n\n3. **Check Session Status**: You can check if your session is still valid:\n ```python\n status = client.call_tool(\"session_status\", {\"session_id\": session_id})\n # Returns information about session validity and remaining time\n ```\n\n4. **Logout**: When finished, you can logout to terminate the session:\n ```python\n client.call_tool(\"logout\", {\"session_id\": session_id})\n ```\n\n### Example: Complete Project Creation Workflow\n\nHere's a complete example of creating a project with epics and user stories:\n\n```python\nfrom mcp.client import Client\n\n# Initialize MCP client\nclient = Client()\n\n# Authenticate and get session ID\nauth_result = client.call_tool(\"login\", {\n \"username\": \"admin\",\n \"password\": \"password123\",\n \"host\": \"https://taiga.mycompany.com\"\n})\nsession_id = auth_result[\"session_id\"]\n\n# Create a new project\nproject = client.call_tool(\"create_project\", {\n \"session_id\": session_id,\n \"name\": \"My New Project\",\n \"description\": \"A test project created via MCP\"\n})\nproject_id = project[\"id\"]\n\n# Create an epic\nepic = client.call_tool(\"create_epic\", {\n \"session_id\": session_id,\n \"project_id\": project_id,\n \"subject\": \"User Authentication\",\n \"description\": \"Implement user authentication features\"\n})\nepic_id = epic[\"id\"]\n\n# Create a user story in the epic\nstory = client.call_tool(\"create_user_story\", {\n \"session_id\": session_id,\n \"project_id\": project_id,\n \"subject\": \"User Login\",\n \"description\": \"As a user, I want to log in with my credentials\",\n \"epic_id\": epic_id\n})\n\n# Logout when done\nclient.call_tool(\"logout\", {\"session_id\": session_id})\n```\n\n## Development\n\n### Project Structure\n\n```\npyTaigaMCP/\n├── src/\n│ ├── server.py # MCP server implementation with tools\n│ ├── taiga_client.py # Taiga API client with all CRUD operations\n│ ├── tools.py # MCP tools definitions\n│ └── config.py # Configuration settings with Pydantic\n├── tests/\n│ ├── conftest.py # Shared pytest fixtures\n│ ├── unit/ # Unit tests\n│ └── integration/ # Integration tests\n├── pyproject.toml # Project configuration and dependencies\n├── install.sh # Installation script\n├── run.sh # Server execution script\n└── README.md # Project documentation\n```\n\n### Testing\n\nRun tests with pytest:\n\n```bash\n# Run all tests\npytest\n\n# Run only unit tests\npytest tests/unit/\n\n# Run only integration tests\npytest tests/integration/\n\n# Run tests with specific markers\npytest -m \"auth\" # Authentication tests\npytest -m \"core\" # Core functionality tests\n\n# Run tests with coverage reporting\npytest --cov=src\n```\n\n### Debugging and Inspection\n\nUse the included inspector tool for debugging:\n\n```bash\n# Default stdio transport\n./inspect.sh\n\n# For SSE transport\n./inspect.sh --sse\n\n# For development mode\n./inspect.sh --dev\n```\n\n## Error Handling\n\nAll API operations return standardized error responses in the following format:\n\n```json\n{\n \"status\": \"error\",\n \"error_type\": \"ExceptionClassName\",\n \"message\": \"Detailed error message\"\n}\n```\n\n## Performance Considerations\n\nThe bridge implements several performance optimizations:\n\n1. **Connection Pooling**: Reuses HTTP connections for better performance\n2. **Rate Limiting**: Prevents overloading the Taiga API\n3. **Retry Mechanism**: Automatically retries failed requests with exponential backoff\n4. **Session Cleanup**: Regularly cleans up expired sessions to free resources\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/amazing-feature`)\n3. Install development dependencies (`./install.sh --dev`)\n4. Make your changes\n5. Run tests (`pytest`)\n6. Commit your changes (`git commit -m 'Add some amazing feature'`)\n7. Push to the branch (`git push origin feature/amazing-feature`)\n8. Open a Pull Request\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## Acknowledgments\n\n- [Taiga](https://www.taiga.io/) for their excellent project management platform\n- [Model Context Protocol (MCP)](https://github.com/mcp-foundation/specification) for the standardized AI communication framework\n- All contributors who have helped shape this project\n","English","tag","en-US",{"_27":39,"_31":43,"_36":44},{"_29":40,"_31":41,"_33":42},"Un puente de protocolo que conecta los sistemas de IA con la plataforma de gestión de proyectos Taiga, lo que permite que las herramientas de IA creen y gestionen proyectos, epopeyas, historias de usuarios, tareas, problemas y sprints.","Puente Taiga MCP","# Puente Taiga MCP\n\n[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/) [![Licencia: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)\n\n## Descripción general\n\nEl puente Taiga MCP es una poderosa capa de integración que conecta la plataforma de gestión de proyectos [Taiga](https://taiga.io/) con el Protocolo de contexto de modelo (MCP), lo que permite que las herramientas y los flujos de trabajo de IA interactúen sin problemas con los recursos de Taiga.\n\nEste puente proporciona un conjunto completo de herramientas y recursos para que los agentes de IA puedan:\n\n* Cree y administre proyectos, epopeyas, historias de usuario, tareas y problemas en Taiga\n* Realizar un seguimiento de sprints y hitos\n* Asignar y actualizar elementos de trabajo\n* Consultar información detallada sobre los artefactos del proyecto\n* Administrar miembros y permisos del proyecto\n\nAl utilizar el estándar MCP, este puente permite que los sistemas de IA mantengan conocimiento contextual sobre el estado del proyecto y realicen tareas complejas de gestión de proyectos de forma programada.\n\n## Características\n\n### Soporte integral de recursos\n\nEl puente admite los siguientes recursos de Taiga con operaciones CRUD completas:\n\n* **Proyectos** : crear, actualizar y administrar configuraciones y metadatos del proyecto\n* **Épicas** : administre funciones grandes que abarcan varios sprints\n* **Historias de usuario** : Manejar requisitos detallados y criterios de aceptación\n* **Tareas** : Realizar un seguimiento de unidades de trabajo más pequeñas dentro de las historias de usuario\n* **Problemas** : gestionar errores, preguntas y solicitudes de mejora\n* **Sprints (hitos)** : planifique y realice un seguimiento del trabajo en intervalos de tiempo definidos\n\n## Instalación\n\nEste proyecto utiliza [uv](https://github.com/astral-sh/uv) para una gestión rápida y confiable de paquetes de Python.\n\n### Prerrequisitos\n\n* Python 3.10 o superior\n* administrador de paquetes uv\n\n### Instalación básica\n\n```bash\n# Clone the repository\ngit clone https://github.com/your-org/pyTaigaMCP.git\ncd pyTaigaMCP\n\n# Install dependencies\n./install.sh\n```\n\n### Instalación de desarrollo\n\nPara desarrollo (incluye herramientas de prueba y calidad de código):\n\n```bash\n./install.sh --dev\n```\n\n### Instalación manual\n\nSi prefieres instalarlo manualmente:\n\n```bash\n# Production dependencies only\nuv pip install -e .\n\n# With development dependencies\nuv pip install -e \".[dev]\"\n```\n\n## Configuración\n\nEl puente se puede configurar a través de variables de entorno o un archivo `.env` :\n\n| Variable de entorno | Descripción | Por defecto |\n| --------------------------- | --------------------------------------------------- | ----------------------- |\n| `TAIGA_API_URL` | URL base para la API de Taiga | |\n| `SESSION_EXPIRY` | Tiempo de expiración de la sesión en segundos | 28800 (8 horas) |\n| `TAIGA_TRANSPORT` | Modo de transporte (stdio o sse) | estudio |\n| `REQUEST_TIMEOUT` | Tiempo de espera de la solicitud de API en segundos | 30 |\n| `MAX_CONNECTIONS` | Número máximo de conexiones HTTP | 10 |\n| `MAX_KEEPALIVE_CONNECTIONS` | Máximo de conexiones keepalive | 5 |\n| `RATE_LIMIT_REQUESTS` | Máximo de solicitudes por minuto | 100 |\n| `LOG_LEVEL` | Nivel de registro | INFORMACIÓN |\n| `LOG_FILE` | Ruta al archivo de registro | taiga\\_mcp.log |\n\nCree un archivo `.env` en la raíz del proyecto para establecer estos valores:\n\n```\nTAIGA_API_URL=https://api.taiga.io/api/v1/\nTAIGA_TRANSPORT=sse\nLOG_LEVEL=DEBUG\n```\n\n## Uso\n\n### Con modo stdio\n\nPegue el siguiente json en la sección de configuración mcp de su aplicación Claude o Cursor:\n\n```json\n{\n \"mcpServers\": {\n \"taigaApi\": {\n \"command\": \"uv\",\n \"args\": [\n \"--directory\",\n \"\",\n \"run\",\n \"src/server.py\"\n ],\n \"env\": {\n \"TAIGA_TRANSPORT\": \"\", \n \"TAIGA_API_URL\": \"\",\n \"TAIGA_PASSWORD\": \"\"\n }\n }\n}\n```\n\n### Corriendo el Puente\n\nInicie el servidor MCP con:\n\n```bash\n# Default stdio transport\n./run.sh\n\n# For SSE transport\n./run.sh --sse\n```\n\nO manualmente:\n\n```bash\n# For stdio transport (default)\nuv run python src/server.py\n\n# For SSE transport\nuv run python src/server.py --sse\n```\n\n### Modos de transporte\n\nEl servidor admite dos modos de transporte:\n\n1. **stdio (Entrada/Salida estándar)** : modo predeterminado para clientes basados en terminal\n2. **SSE (Eventos enviados por el servidor)** : transporte basado en web con capacidades de envío al servidor\n\nPuede configurar el modo de transporte de varias maneras:\n\n* Usando el indicador `--sse` con run.sh o server.py (el valor predeterminado es stdio)\n* Configuración de la variable de entorno `TAIGA_TRANSPORT`\n* Agregar `TAIGA_TRANSPORT=sse` a su archivo `.env`\n\n### Flujo de autenticación\n\nEste puente MCP utiliza un modelo de autenticación basado en sesión:\n\n1. **Iniciar sesión** : Los clientes primero deben autenticarse utilizando la herramienta `login` :\n\n ```python\n session = client.call_tool(\"login\", {\n \"username\": \"your_taiga_username\",\n \"password\": \"your_taiga_password\",\n \"host\": \"https://api.taiga.io\" # Optional\n })\n # Save the session_id from the response\n session_id = session[\"session_id\"]\n ```\n\n2. **Uso de herramientas y recursos** : Incluya el `session_id` en cada llamada API:\n\n ```python\n # For resources, include session_id in the URI\n projects = client.get_resource(f\"taiga://projects?session_id={session_id}\")\n\n # For project-specific resources\n epics = client.get_resource(f\"taiga://projects/123/epics?session_id={session_id}\")\n\n # For tools, include session_id as a parameter\n new_project = client.call_tool(\"create_project\", {\n \"session_id\": session_id,\n \"name\": \"New Project\",\n \"description\": \"Description\"\n })\n ```\n\n3. **Comprobar el estado de la sesión** : Puedes comprobar si tu sesión sigue siendo válida:\n\n ```python\n status = client.call_tool(\"session_status\", {\"session_id\": session_id})\n # Returns information about session validity and remaining time\n ```\n\n4. **Cerrar sesión** : cuando haya terminado, puede cerrar la sesión para finalizar la sesión:\n\n ```python\n client.call_tool(\"logout\", {\"session_id\": session_id})\n ```\n\n### Ejemplo: Flujo de trabajo completo de creación de proyectos\n\nA continuación se muestra un ejemplo completo de creación de un proyecto con epopeyas e historias de usuario:\n\n```python\nfrom mcp.client import Client\n\n# Initialize MCP client\nclient = Client()\n\n# Authenticate and get session ID\nauth_result = client.call_tool(\"login\", {\n \"username\": \"admin\",\n \"password\": \"password123\",\n \"host\": \"https://taiga.mycompany.com\"\n})\nsession_id = auth_result[\"session_id\"]\n\n# Create a new project\nproject = client.call_tool(\"create_project\", {\n \"session_id\": session_id,\n \"name\": \"My New Project\",\n \"description\": \"A test project created via MCP\"\n})\nproject_id = project[\"id\"]\n\n# Create an epic\nepic = client.call_tool(\"create_epic\", {\n \"session_id\": session_id,\n \"project_id\": project_id,\n \"subject\": \"User Authentication\",\n \"description\": \"Implement user authentication features\"\n})\nepic_id = epic[\"id\"]\n\n# Create a user story in the epic\nstory = client.call_tool(\"create_user_story\", {\n \"session_id\": session_id,\n \"project_id\": project_id,\n \"subject\": \"User Login\",\n \"description\": \"As a user, I want to log in with my credentials\",\n \"epic_id\": epic_id\n})\n\n# Logout when done\nclient.call_tool(\"logout\", {\"session_id\": session_id})\n```\n\n## Desarrollo\n\n### Estructura del proyecto\n\n```\npyTaigaMCP/\n├── src/\n│ ├── server.py # MCP server implementation with tools\n│ ├── taiga_client.py # Taiga API client with all CRUD operations\n│ ├── tools.py # MCP tools definitions\n│ └── config.py # Configuration settings with Pydantic\n├── tests/\n│ ├── conftest.py # Shared pytest fixtures\n│ ├── unit/ # Unit tests\n│ └── integration/ # Integration tests\n├── pyproject.toml # Project configuration and dependencies\n├── install.sh # Installation script\n├── run.sh # Server execution script\n└── README.md # Project documentation\n```\n\n### Pruebas\n\nEjecutar pruebas con pytest:\n\n```bash\n# Run all tests\npytest\n\n# Run only unit tests\npytest tests/unit/\n\n# Run only integration tests\npytest tests/integration/\n\n# Run tests with specific markers\npytest -m \"auth\" # Authentication tests\npytest -m \"core\" # Core functionality tests\n\n# Run tests with coverage reporting\npytest --cov=src\n```\n\n### Depuración e inspección\n\nUtilice la herramienta de inspección incluida para depurar:\n\n```bash\n# Default stdio transport\n./inspect.sh\n\n# For SSE transport\n./inspect.sh --sse\n\n# For development mode\n./inspect.sh --dev\n```\n\n## Manejo de errores\n\nTodas las operaciones de API devuelven respuestas de error estandarizadas en el siguiente formato:\n\n```json\n{\n \"status\": \"error\",\n \"error_type\": \"ExceptionClassName\",\n \"message\": \"Detailed error message\"\n}\n```\n\n## Consideraciones de rendimiento\n\nEl puente implementa varias optimizaciones de rendimiento:\n\n1. **Agrupación de conexiones** : reutiliza las conexiones HTTP para un mejor rendimiento\n2. **Limitación de velocidad** : evita la sobrecarga de la API de Taiga\n3. **Mecanismo de reintento** : reintenta automáticamente las solicitudes fallidas con retroceso exponencial\n4. **Limpieza de sesiones** : limpia periódicamente las sesiones caducadas para liberar recursos\n\n## Contribuyendo\n\n¡Agradecemos sus contribuciones! No dude en enviar una solicitud de incorporación de cambios.\n\n1. Bifurcar el repositorio\n2. Crea tu rama de funciones ( `git checkout -b feature/amazing-feature` )\n3. Instalar dependencias de desarrollo ( `./install.sh --dev` )\n4. Realiza tus cambios\n5. Ejecutar pruebas ( `pytest` )\n6. Confirme sus cambios ( `git commit -m 'Add some amazing feature'` )\n7. Empujar a la rama ( `git push origin feature/amazing-feature` )\n8. Abrir una solicitud de extracción\n\n## Licencia\n\nEste proyecto está licenciado bajo la licencia MIT: consulte el archivo de LICENCIA para obtener más detalles.\n\n## Expresiones de gratitud\n\n* [Taiga](https://www.taiga.io/) por su excelente plataforma de gestión de proyectos\n* [Protocolo de contexto de modelo (MCP)](https://github.com/mcp-foundation/specification) para el marco de comunicación de IA estandarizado\n* Todos los colaboradores que han ayudado a dar forma a este proyecto","Spanish","es-ES",{"_27":46,"_31":50,"_36":51},{"_29":47,"_31":48,"_33":49},"AI システムを Taiga プロジェクト管理プラットフォームに接続し、AI ツールがプロジェクト、エピック、ユーザーストーリー、タスク、問題、スプリントを作成および管理できるようにするプロトコルブリッジ。","タイガMCP橋","# タイガMCP橋\n\n[![Python 3.10以上](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/) [![ライセンス: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)\n\n## 概要\n\nTaiga MCP ブリッジは、 [Taiga](https://taiga.io/)プロジェクト管理プラットフォームとモデルコンテキストプロトコル (MCP) を接続する強力な統合レイヤーであり、AI ツールとワークフローが Taiga のリソースとシームレスに対話できるようにします。\n\nこのブリッジは、AI エージェントに次のことを行うための包括的なツールとリソースのセットを提供します。\n\n* Taiga でプロジェクト、エピック、ユーザーストーリー、タスク、問題を作成および管理します\n* スプリントとマイルストーンを追跡する\n* 作業項目の割り当てと更新\n* プロジェクト成果物に関する詳細情報を照会する\n* プロジェクトメンバーと権限を管理する\n\nこのブリッジでは、MCP 標準を使用することで、AI システムがプロジェクトの状態に関するコンテキスト認識を維持し、複雑なプロジェクト管理タスクをプログラムで実行できるようになります。\n\n## 特徴\n\n### 包括的なリソースサポート\n\nブリッジは、完全な CRUD 操作を備えた次の Taiga リソースをサポートします。\n\n* **プロジェクト**: プロジェクト設定とメタデータを作成、更新、管理します\n* **エピック**: 複数のスプリントにまたがる大規模な機能を管理します\n* **ユーザーストーリー**: 詳細な要件と受け入れ基準を処理する\n* **タスク**: ユーザーストーリー内の小さな作業単位を追跡する\n* **問題**: バグ、質問、機能強化のリクエストを管理する\n* **スプリント（マイルストーン）** ：時間枠で区切られた間隔で作業を計画し、追跡します\n\n## インストール\n\nこのプロジェクトでは、高速で信頼性の高い Python パッケージ管理のために[uv](https://github.com/astral-sh/uv)を使用します。\n\n### 前提条件\n\n* Python 3.10以上\n* UVパッケージマネージャー\n\n### 基本的なインストール\n\n```bash\n# Clone the repository\ngit clone https://github.com/your-org/pyTaigaMCP.git\ncd pyTaigaMCP\n\n# Install dependencies\n./install.sh\n```\n\n### 開発インストール\n\n開発用（テストおよびコード品質ツールを含む）:\n\n```bash\n./install.sh --dev\n```\n\n### 手動インストール\n\n手動でインストールする場合:\n\n```bash\n# Production dependencies only\nuv pip install -e .\n\n# With development dependencies\nuv pip install -e \".[dev]\"\n```\n\n## 構成\n\nブリッジは環境変数または`.env`ファイルを通じて設定できます。\n\n| 環境変数 | 説明 | デフォルト |\n| --------------------------- | ------------------------- | ----------------------- |\n| `TAIGA_API_URL` | Taiga API のベース URL | |\n| `SESSION_EXPIRY` | セッションの有効期限（秒） | 28800（8時間） |\n| `TAIGA_TRANSPORT` | トランスポートモード（stdio または sse） | 標準入出力 |\n| `REQUEST_TIMEOUT` | APIリクエストのタイムアウト（秒） | 30 |\n| `MAX_CONNECTIONS` | HTTP接続の最大数 | 10 |\n| `MAX_KEEPALIVE_CONNECTIONS` | 最大キープアライブ接続数 | 5 |\n| `RATE_LIMIT_REQUESTS` | 1分あたりの最大リクエスト数 | 100 |\n| `LOG_LEVEL` | ログレベル | 情報 |\n| `LOG_FILE` | ログファイルへのパス | taiga\\_mcp.log |\n\nプロジェクトルートに`.env`ファイルを作成し、次の値を設定します。\n\n```\nTAIGA_API_URL=https://api.taiga.io/api/v1/\nTAIGA_TRANSPORT=sse\nLOG_LEVEL=DEBUG\n```\n\n## 使用法\n\n### stdioモードの場合\n\n次の json を Claude アプリまたはカーソルの mcp 設定セクションに貼り付けます。\n\n```json\n{\n \"mcpServers\": {\n \"taigaApi\": {\n \"command\": \"uv\",\n \"args\": [\n \"--directory\",\n \"\",\n \"run\",\n \"src/server.py\"\n ],\n \"env\": {\n \"TAIGA_TRANSPORT\": \"\", \n \"TAIGA_API_URL\": \"\",\n \"TAIGA_PASSWORD\": \"\"\n }\n }\n}\n```\n\n### 橋を走る\n\n次のコマンドで MCP サーバーを起動します。\n\n```bash\n# Default stdio transport\n./run.sh\n\n# For SSE transport\n./run.sh --sse\n```\n\nまたは手動で:\n\n```bash\n# For stdio transport (default)\nuv run python src/server.py\n\n# For SSE transport\nuv run python src/server.py --sse\n```\n\n### 輸送モード\n\nサーバーは 2 つのトランスポートモードをサポートしています。\n\n1. **stdio (標準入出力)** - 端末ベースのクライアントのデフォルトモード\n2. **SSE (Server-Sent Events)** - サーバープッシュ機能を備えたWebベースのトランスポート\n\nトランスポートモードはいくつかの方法で設定できます。\n\n* run.sh または server.py で`--sse`フラグを使用する (デフォルトは stdio)\n* `TAIGA_TRANSPORT`環境変数の設定\n* `.env`ファイルに`TAIGA_TRANSPORT=sse`を追加する\n\n### 認証フロー\n\nこの MCP ブリッジはセッションベースの認証モデルを使用します。\n\n1. **ログイン**: クライアントはまず`login`ツールを使用して認証する必要があります。\n\n ```python\n session = client.call_tool(\"login\", {\n \"username\": \"your_taiga_username\",\n \"password\": \"your_taiga_password\",\n \"host\": \"https://api.taiga.io\" # Optional\n })\n # Save the session_id from the response\n session_id = session[\"session_id\"]\n ```\n\n2. **ツールとリソースの使用**: すべての API 呼び出しに`session_id`を含めます。\n\n ```python\n # For resources, include session_id in the URI\n projects = client.get_resource(f\"taiga://projects?session_id={session_id}\")\n\n # For project-specific resources\n epics = client.get_resource(f\"taiga://projects/123/epics?session_id={session_id}\")\n\n # For tools, include session_id as a parameter\n new_project = client.call_tool(\"create_project\", {\n \"session_id\": session_id,\n \"name\": \"New Project\",\n \"description\": \"Description\"\n })\n ```\n\n3. **セッションステータスの確認**: セッションがまだ有効かどうかを確認できます。\n\n ```python\n status = client.call_tool(\"session_status\", {\"session_id\": session_id})\n # Returns information about session validity and remaining time\n ```\n\n4. **ログアウト**: 終了したら、ログアウトしてセッションを終了できます。\n\n ```python\n client.call_tool(\"logout\", {\"session_id\": session_id})\n ```\n\n### 例: 完全なプロジェクト作成ワークフロー\n\nエピックとユーザーストーリーを使用してプロジェクトを作成する完全な例を次に示します。\n\n```python\nfrom mcp.client import Client\n\n# Initialize MCP client\nclient = Client()\n\n# Authenticate and get session ID\nauth_result = client.call_tool(\"login\", {\n \"username\": \"admin\",\n \"password\": \"password123\",\n \"host\": \"https://taiga.mycompany.com\"\n})\nsession_id = auth_result[\"session_id\"]\n\n# Create a new project\nproject = client.call_tool(\"create_project\", {\n \"session_id\": session_id,\n \"name\": \"My New Project\",\n \"description\": \"A test project created via MCP\"\n})\nproject_id = project[\"id\"]\n\n# Create an epic\nepic = client.call_tool(\"create_epic\", {\n \"session_id\": session_id,\n \"project_id\": project_id,\n \"subject\": \"User Authentication\",\n \"description\": \"Implement user authentication features\"\n})\nepic_id = epic[\"id\"]\n\n# Create a user story in the epic\nstory = client.call_tool(\"create_user_story\", {\n \"session_id\": session_id,\n \"project_id\": project_id,\n \"subject\": \"User Login\",\n \"description\": \"As a user, I want to log in with my credentials\",\n \"epic_id\": epic_id\n})\n\n# Logout when done\nclient.call_tool(\"logout\", {\"session_id\": session_id})\n```\n\n## 発達\n\n### プロジェクト構造\n\n```\npyTaigaMCP/\n├── src/\n│ ├── server.py # MCP server implementation with tools\n│ ├── taiga_client.py # Taiga API client with all CRUD operations\n│ ├── tools.py # MCP tools definitions\n│ └── config.py # Configuration settings with Pydantic\n├── tests/\n│ ├── conftest.py # Shared pytest fixtures\n│ ├── unit/ # Unit tests\n│ └── integration/ # Integration tests\n├── pyproject.toml # Project configuration and dependencies\n├── install.sh # Installation script\n├── run.sh # Server execution script\n└── README.md # Project documentation\n```\n\n### テスト\n\npytest でテストを実行します。\n\n```bash\n# Run all tests\npytest\n\n# Run only unit tests\npytest tests/unit/\n\n# Run only integration tests\npytest tests/integration/\n\n# Run tests with specific markers\npytest -m \"auth\" # Authentication tests\npytest -m \"core\" # Core functionality tests\n\n# Run tests with coverage reporting\npytest --cov=src\n```\n\n### デバッグと検査\n\nデバッグには付属のインスペクタツールを使用します。\n\n```bash\n# Default stdio transport\n./inspect.sh\n\n# For SSE transport\n./inspect.sh --sse\n\n# For development mode\n./inspect.sh --dev\n```\n\n## エラー処理\n\nすべての API 操作は、次の形式で標準化されたエラー応答を返します。\n\n```json\n{\n \"status\": \"error\",\n \"error_type\": \"ExceptionClassName\",\n \"message\": \"Detailed error message\"\n}\n```\n\n## パフォーマンスに関する考慮事項\n\nブリッジはいくつかのパフォーマンス最適化を実装します。\n\n1. **接続プーリング**: HTTP接続を再利用してパフォーマンスを向上します\n2. **レート制限**: Taiga API の過負荷を防止します\n3. **再試行メカニズム**: 指数バックオフを使用して失敗したリクエストを自動的に再試行します\n4. **セッションクリーンアップ**: 期限切れのセッションを定期的にクリーンアップしてリソースを解放します\n\n## 貢献\n\n貢献を歓迎します！お気軽にプルリクエストを送信してください。\n\n1. リポジトリをフォークする\n2. 機能ブランチを作成します（ `git checkout -b feature/amazing-feature` ）\n3. 開発依存関係をインストールする ( `./install.sh --dev` )\n4. 変更を加える\n5. テストを実行する ( `pytest` )\n6. 変更をコミットします ( `git commit -m 'Add some amazing feature'` )\n7. ブランチにプッシュする ( `git push origin feature/amazing-feature` )\n8. プルリクエストを開く\n\n## ライセンス\n\nこのプロジェクトは MIT ライセンスに基づいてライセンスされています - 詳細については LICENSE ファイルを参照してください。\n\n## 謝辞\n\n* 優れたプロジェクト管理プラットフォームを提供する[Taiga](https://www.taiga.io/)\n* 標準化された AI 通信フレームワークの[モデルコンテキストプロトコル (MCP)](https://github.com/mcp-foundation/specification)\n* このプロジェクトの形成にご協力いただいたすべての貢献者","Japanese","ja-JP",{"_27":53,"_31":57,"_36":58},{"_29":54,"_31":55,"_33":56},"AI 시스템을 Taiga 프로젝트 관리 플랫폼에 연결하는 프로토콜 브리지로, AI 도구가 프로젝트, 에픽, 사용자 스토리, 작업, 이슈 및 스프린트를 생성하고 관리할 수 있도록 합니다.","타이가 MCP 다리","# 타이가 MCP 다리\n\n[![파이썬 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/) [![라이센스: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)\n\n## 개요\n\nTaiga MCP Bridge는 [Taiga](https://taiga.io/) 프로젝트 관리 플랫폼을 MCP(Model Context Protocol)와 연결하는 강력한 통합 계층으로, AI 도구와 워크플로가 Taiga 리소스와 원활하게 상호 작용할 수 있도록 합니다.\n\n이 브리지는 AI 에이전트에게 다음을 위한 포괄적인 도구와 리소스 세트를 제공합니다.\n\n* Taiga에서 프로젝트, 에픽, 사용자 스토리, 작업 및 이슈를 생성하고 관리합니다.\n* 스프린트와 이정표 추적\n* 작업 항목 할당 및 업데이트\n* 프로젝트 아티팩트에 대한 자세한 정보 쿼리\n* 프로젝트 멤버 및 권한 관리\n\n이 브리지는 MCP 표준을 사용하여 AI 시스템이 프로젝트 상태에 대한 상황적 인식을 유지하고 복잡한 프로젝트 관리 작업을 프로그래밍 방식으로 수행할 수 있도록 합니다.\n\n## 특징\n\n### 포괄적인 리소스 지원\n\n브리지는 완전한 CRUD 작업을 통해 다음 Taiga 리소스를 지원합니다.\n\n* **프로젝트** : 프로젝트 설정 및 메타데이터를 생성, 업데이트 및 관리합니다.\n* **에픽** : 여러 스프린트에 걸쳐 있는 대규모 기능 관리\n* **사용자 스토리** : 세부적인 요구 사항 및 승인 기준 처리\n* **작업** : 사용자 스토리 내에서 더 작은 작업 단위를 추적합니다.\n* **이슈** : 버그, 질문, 개선 요청 관리\n* **스프린트(마일스톤)** : 시간 상자 간격으로 작업을 계획하고 추적합니다.\n\n## 설치\n\n이 프로젝트에서는 빠르고 안정적인 Python 패키지 관리를 위해 [uv를](https://github.com/astral-sh/uv) 사용합니다.\n\n### 필수 조건\n\n* Python 3.10 이상\n* uv 패키지 관리자\n\n### 기본 설치\n\n지엑스피1\n\n### 개발 설치\n\n개발용(테스트 및 코드 품질 도구 포함):\n\n```bash\n./install.sh --dev\n```\n\n### 수동 설치\n\n수동으로 설치하려면 다음을 수행하세요.\n\n```bash\n# Production dependencies only\nuv pip install -e .\n\n# With development dependencies\nuv pip install -e \".[dev]\"\n```\n\n## 구성\n\n브리지는 환경 변수나 `.env` 파일을 통해 구성할 수 있습니다.\n\n| 환경 변수 | 설명 | 기본 |\n| --------------------------- | ------------------- | ----------------------- |\n| `TAIGA_API_URL` | Taiga API의 기본 URL | |\n| `SESSION_EXPIRY` | 세션 만료 시간(초) | 28800(8시간) |\n| `TAIGA_TRANSPORT` | 전송 모드(stdio 또는 sse) | stdio |\n| `REQUEST_TIMEOUT` | API 요청 시간 초과(초) | 30 |\n| `MAX_CONNECTIONS` | 최대 HTTP 연결 수 | 10 |\n| `MAX_KEEPALIVE_CONNECTIONS` | 최대 keepalive 연결 | 5 |\n| `RATE_LIMIT_REQUESTS` | 분당 최대 요청 수 | 100 |\n| `LOG_LEVEL` | 로깅 레벨 | 정보 |\n| `LOG_FILE` | 로그 파일 경로 | taiga\\_mcp.log |\n\n프로젝트 루트에 `.env` 파일을 만들어 다음 값을 설정합니다.\n\n```\nTAIGA_API_URL=https://api.taiga.io/api/v1/\nTAIGA_TRANSPORT=sse\nLOG_LEVEL=DEBUG\n```\n\n## 용법\n\n### stdio 모드로\n\n다음 json을 Claude 앱이나 커서의 mcp 설정 섹션에 붙여넣으세요.\n\n```json\n{\n \"mcpServers\": {\n \"taigaApi\": {\n \"command\": \"uv\",\n \"args\": [\n \"--directory\",\n \"\",\n \"run\",\n \"src/server.py\"\n ],\n \"env\": {\n \"TAIGA_TRANSPORT\": \"\", \n \"TAIGA_API_URL\": \"\",\n \"TAIGA_PASSWORD\": \"\"\n }\n }\n}\n```\n\n### 다리 달리기\n\n다음을 사용하여 MCP 서버를 시작합니다.\n\n```bash\n# Default stdio transport\n./run.sh\n\n# For SSE transport\n./run.sh --sse\n```\n\n또는 수동으로:\n\n```bash\n# For stdio transport (default)\nuv run python src/server.py\n\n# For SSE transport\nuv run python src/server.py --sse\n```\n\n### 운송 모드\n\n서버는 두 가지 전송 모드를 지원합니다.\n\n1. **stdio(표준 입출력)** - 터미널 기반 클라이언트를 위한 기본 모드\n2. **SSE(Server-Sent Events)** - 서버 푸시 기능을 갖춘 웹 기반 전송\n\n운송 모드는 여러 가지 방법으로 설정할 수 있습니다.\n\n* run.sh 또는 server.py와 함께 `--sse` 플래그 사용(기본값은 stdio)\n* `TAIGA_TRANSPORT` 환경 변수 설정\n* `.env` 파일에 `TAIGA_TRANSPORT=sse` 추가\n\n### 인증 흐름\n\n이 MCP 브리지는 세션 기반 인증 모델을 사용합니다.\n\n1. **로그인** : 클라이언트는 먼저 `login` 도구를 사용하여 인증해야 합니다.\n\n ```python\n session = client.call_tool(\"login\", {\n \"username\": \"your_taiga_username\",\n \"password\": \"your_taiga_password\",\n \"host\": \"https://api.taiga.io\" # Optional\n })\n # Save the session_id from the response\n session_id = session[\"session_id\"]\n ```\n\n2. **도구 및 리소스 사용** : 모든 API 호출에 `session_id` 포함합니다.\n\n ```python\n # For resources, include session_id in the URI\n projects = client.get_resource(f\"taiga://projects?session_id={session_id}\")\n\n # For project-specific resources\n epics = client.get_resource(f\"taiga://projects/123/epics?session_id={session_id}\")\n\n # For tools, include session_id as a parameter\n new_project = client.call_tool(\"create_project\", {\n \"session_id\": session_id,\n \"name\": \"New Project\",\n \"description\": \"Description\"\n })\n ```\n\n3. **세션 상태 확인** : 세션이 여전히 유효한지 확인할 수 있습니다.\n\n ```python\n status = client.call_tool(\"session_status\", {\"session_id\": session_id})\n # Returns information about session validity and remaining time\n ```\n\n4. **로그아웃** : 작업이 끝나면 로그아웃하여 세션을 종료할 수 있습니다.\n\n ```python\n client.call_tool(\"logout\", {\"session_id\": session_id})\n ```\n\n### 예: 프로젝트 생성 워크플로 완료\n\n에픽과 사용자 스토리를 사용하여 프로젝트를 만드는 전체 예는 다음과 같습니다.\n\n```python\nfrom mcp.client import Client\n\n# Initialize MCP client\nclient = Client()\n\n# Authenticate and get session ID\nauth_result = client.call_tool(\"login\", {\n \"username\": \"admin\",\n \"password\": \"password123\",\n \"host\": \"https://taiga.mycompany.com\"\n})\nsession_id = auth_result[\"session_id\"]\n\n# Create a new project\nproject = client.call_tool(\"create_project\", {\n \"session_id\": session_id,\n \"name\": \"My New Project\",\n \"description\": \"A test project created via MCP\"\n})\nproject_id = project[\"id\"]\n\n# Create an epic\nepic = client.call_tool(\"create_epic\", {\n \"session_id\": session_id,\n \"project_id\": project_id,\n \"subject\": \"User Authentication\",\n \"description\": \"Implement user authentication features\"\n})\nepic_id = epic[\"id\"]\n\n# Create a user story in the epic\nstory = client.call_tool(\"create_user_story\", {\n \"session_id\": session_id,\n \"project_id\": project_id,\n \"subject\": \"User Login\",\n \"description\": \"As a user, I want to log in with my credentials\",\n \"epic_id\": epic_id\n})\n\n# Logout when done\nclient.call_tool(\"logout\", {\"session_id\": session_id})\n```\n\n## 개발\n\n### 프로젝트 구조\n\n```\npyTaigaMCP/\n├── src/\n│ ├── server.py # MCP server implementation with tools\n│ ├── taiga_client.py # Taiga API client with all CRUD operations\n│ ├── tools.py # MCP tools definitions\n│ └── config.py # Configuration settings with Pydantic\n├── tests/\n│ ├── conftest.py # Shared pytest fixtures\n│ ├── unit/ # Unit tests\n│ └── integration/ # Integration tests\n├── pyproject.toml # Project configuration and dependencies\n├── install.sh # Installation script\n├── run.sh # Server execution script\n└── README.md # Project documentation\n```\n\n### 테스트\n\npytest로 테스트 실행:\n\n```bash\n# Run all tests\npytest\n\n# Run only unit tests\npytest tests/unit/\n\n# Run only integration tests\npytest tests/integration/\n\n# Run tests with specific markers\npytest -m \"auth\" # Authentication tests\npytest -m \"core\" # Core functionality tests\n\n# Run tests with coverage reporting\npytest --cov=src\n```\n\n### 디버깅 및 검사\n\n디버깅을 위해 포함된 검사기 도구를 사용하세요.\n\n```bash\n# Default stdio transport\n./inspect.sh\n\n# For SSE transport\n./inspect.sh --sse\n\n# For development mode\n./inspect.sh --dev\n```\n\n## 오류 처리\n\n모든 API 작업은 다음 형식으로 표준화된 오류 응답을 반환합니다.\n\n```json\n{\n \"status\": \"error\",\n \"error_type\": \"ExceptionClassName\",\n \"message\": \"Detailed error message\"\n}\n```\n\n## 성능 고려 사항\n\n브리지는 여러 가지 성능 최적화를 구현합니다.\n\n1. **연결 풀링** : 더 나은 성능을 위해 HTTP 연결을 재사용합니다.\n2. **속도 제한** : Taiga API 과부하 방지\n3. **재시도 메커니즘** : 지수적 백오프를 통해 실패한 요청을 자동으로 재시도합니다.\n4. **세션 정리** : 만료된 세션을 정기적으로 정리하여 리소스를 확보합니다.\n\n## 기여하다\n\n기여를 환영합니다! 풀 리퀘스트를 제출해 주세요.\n\n1. 저장소를 포크하세요\n2. 기능 브랜치를 생성합니다( `git checkout -b feature/amazing-feature` )\n3. 개발 종속성 설치( `./install.sh --dev` )\n4. 변경 사항을 만드세요\n5. 테스트 실행( `pytest` )\n6. 변경 사항을 커밋하세요( `git commit -m 'Add some amazing feature'` )\n7. 브랜치에 푸시( `git push origin feature/amazing-feature` )\n8. 풀 리퀘스트 열기\n\n## 특허\n\n이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스 파일을 참조하세요.\n\n## 감사의 말\n\n* 탁월한 프로젝트 관리 플랫폼을 제공하는 [Taiga](https://www.taiga.io/)\n* 표준화된 AI 커뮤니케이션 프레임워크를 위한 [모델 컨텍스트 프로토콜(MCP)](https://github.com/mcp-foundation/specification)\n* 이 프로젝트를 형성하는 데 도움을 준 모든 기여자","Korean","ko-KR",{"_27":60,"_31":64,"_36":65},{"_29":61,"_31":62,"_33":63},"将 AI 系统连接到 Taiga 项目管理平台的协议桥，使 AI 工具能够创建和管理项目、史诗、用户故事、任务、问题和冲刺。","Taiga MCP 桥","# Taiga MCP 桥\n\n[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/) [![许可证：MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)\n\n## 概述\n\nTaiga MCP Bridge 是一个强大的集成层，它将[Taiga](https://taiga.io/)项目管理平台与模型上下文协议 (MCP) 连接起来，使 AI 工具和工作流能够与 Taiga 的资源无缝交互。\n\n这座桥梁为人工智能代理提供了一套全面的工具和资源，可以：\n\n* 在 Taiga 中创建和管理项目、史诗、用户故事、任务和问题\n* 跟踪冲刺和里程碑\n* 分配和更新工作项\n* 查询项目工件的详细信息\n* 管理项目成员和权限\n\n通过使用 MCP 标准，该桥梁允许 AI 系统保持对项目状态的上下文感知并以编程方式执行复杂的项目管理任务。\n\n## 特征\n\n### 全面的资源支持\n\n该桥支持以下 Taiga 资源并具有完整的 CRUD 操作：\n\n* **项目**：创建、更新和管理项目设置和元数据\n* **Epics** ：管理跨越多个冲刺的大型功能\n* **用户故事**：处理详细需求和验收标准\n* **任务**：跟踪用户故事中较小的工作单元\n* **问题**：管理错误、问题和增强请求\n* **冲刺（里程碑）** ：在规定的时间间隔内计划和跟踪工作\n\n## 安装\n\n该项目使用[uv](https://github.com/astral-sh/uv)进行快速、可靠的 Python 包管理。\n\n### 先决条件\n\n* Python 3.10 或更高版本\n* uv 包管理器\n\n### 基本安装\n\n```bash\n# Clone the repository\ngit clone https://github.com/your-org/pyTaigaMCP.git\ncd pyTaigaMCP\n\n# Install dependencies\n./install.sh\n```\n\n### 开发安装\n\n对于开发（包括测试和代码质量工具）：\n\n```bash\n./install.sh --dev\n```\n\n### 手动安装\n\n如果您希望手动安装：\n\n```bash\n# Production dependencies only\nuv pip install -e .\n\n# With development dependencies\nuv pip install -e \".[dev]\"\n```\n\n## 配置\n\n可以通过环境变量或`.env`文件配置桥接器：\n\n| 环境变量 | 描述 | 默认 |\n| --------------------------- | ----------------- | ----------------------- |\n| `TAIGA_API_URL` | Taiga API 的基本 URL | |\n| `SESSION_EXPIRY` | 会话过期时间（以秒为单位） | 28800（8小时） |\n| `TAIGA_TRANSPORT` | 传输模式（stdio 或 sse） | 标准输入输出 |\n| `REQUEST_TIMEOUT` | API 请求超时（秒） | 三十 |\n| `MAX_CONNECTIONS` | 最大 HTTP 连接数 | 10 |\n| `MAX_KEEPALIVE_CONNECTIONS` | 最大保持连接数 | 5 |\n| `RATE_LIMIT_REQUESTS` | 每分钟最大请求数 | 100 |\n| `LOG_LEVEL` | 日志级别 | 信息 |\n| `LOG_FILE` | 日志文件路径 | taiga\\_mcp.log |\n\n在项目根目录中创建一个`.env`文件来设置这些值：\n\n```\nTAIGA_API_URL=https://api.taiga.io/api/v1/\nTAIGA_TRANSPORT=sse\nLOG_LEVEL=DEBUG\n```\n\n## 用法\n\n### 使用 stdio 模式\n\n将以下 json 粘贴到您的 Claude App 或 Cursor 的 mcp 设置部分：\n\n```json\n{\n \"mcpServers\": {\n \"taigaApi\": {\n \"command\": \"uv\",\n \"args\": [\n \"--directory\",\n \"\",\n \"run\",\n \"src/server.py\"\n ],\n \"env\": {\n \"TAIGA_TRANSPORT\": \"\", \n \"TAIGA_API_URL\": \"\",\n \"TAIGA_PASSWORD\": \"\"\n }\n }\n}\n```\n\n### 跑桥\n\n使用以下命令启动 MCP 服务器：\n\n```bash\n# Default stdio transport\n./run.sh\n\n# For SSE transport\n./run.sh --sse\n```\n\n或者手动：\n\n```bash\n# For stdio transport (default)\nuv run python src/server.py\n\n# For SSE transport\nuv run python src/server.py --sse\n```\n\n### 交通方式\n\n服务器支持两种传输模式：\n\n1. **stdio（标准输入/输出）** -基于终端的客户端的默认模式\n2. **SSE（服务器发送事件）** ——具有服务器推送功能的基于 Web 的传输\n\n您可以通过多种方式设置传输模式：\n\n* 将`--sse`标志与 run.sh 或 server.py 一起使用（默认为 stdio）\n* 设置`TAIGA_TRANSPORT`环境变量\n* 将`TAIGA_TRANSPORT=sse`添加到您的`.env`文件\n\n### 身份验证流程\n\n此 MCP 桥使用基于会话的身份验证模型：\n\n1. **登录**：客户端必须首先使用`login`工具进行身份验证：\n\n ```python\n session = client.call_tool(\"login\", {\n \"username\": \"your_taiga_username\",\n \"password\": \"your_taiga_password\",\n \"host\": \"https://api.taiga.io\" # Optional\n })\n # Save the session_id from the response\n session_id = session[\"session_id\"]\n ```\n\n2. **使用工具和资源**：在每个 API 调用中包含`session_id` ：\n\n ```python\n # For resources, include session_id in the URI\n projects = client.get_resource(f\"taiga://projects?session_id={session_id}\")\n\n # For project-specific resources\n epics = client.get_resource(f\"taiga://projects/123/epics?session_id={session_id}\")\n\n # For tools, include session_id as a parameter\n new_project = client.call_tool(\"create_project\", {\n \"session_id\": session_id,\n \"name\": \"New Project\",\n \"description\": \"Description\"\n })\n ```\n\n3. **检查会话状态**：您可以检查会话是否仍然有效：\n\n ```python\n status = client.call_tool(\"session_status\", {\"session_id\": session_id})\n # Returns information about session validity and remaining time\n ```\n\n4. **注销**：完成后，您可以注销以终止会话：\n\n ```python\n client.call_tool(\"logout\", {\"session_id\": session_id})\n ```\n\n### 示例：完整的项目创建工作流程\n\n以下是使用史诗和用户故事创建项目的完整示例：\n\n```python\nfrom mcp.client import Client\n\n# Initialize MCP client\nclient = Client()\n\n# Authenticate and get session ID\nauth_result = client.call_tool(\"login\", {\n \"username\": \"admin\",\n \"password\": \"password123\",\n \"host\": \"https://taiga.mycompany.com\"\n})\nsession_id = auth_result[\"session_id\"]\n\n# Create a new project\nproject = client.call_tool(\"create_project\", {\n \"session_id\": session_id,\n \"name\": \"My New Project\",\n \"description\": \"A test project created via MCP\"\n})\nproject_id = project[\"id\"]\n\n# Create an epic\nepic = client.call_tool(\"create_epic\", {\n \"session_id\": session_id,\n \"project_id\": project_id,\n \"subject\": \"User Authentication\",\n \"description\": \"Implement user authentication features\"\n})\nepic_id = epic[\"id\"]\n\n# Create a user story in the epic\nstory = client.call_tool(\"create_user_story\", {\n \"session_id\": session_id,\n \"project_id\": project_id,\n \"subject\": \"User Login\",\n \"description\": \"As a user, I want to log in with my credentials\",\n \"epic_id\": epic_id\n})\n\n# Logout when done\nclient.call_tool(\"logout\", {\"session_id\": session_id})\n```\n\n## 发展\n\n### 项目结构\n\n```\npyTaigaMCP/\n├── src/\n│ ├── server.py # MCP server implementation with tools\n│ ├── taiga_client.py # Taiga API client with all CRUD operations\n│ ├── tools.py # MCP tools definitions\n│ └── config.py # Configuration settings with Pydantic\n├── tests/\n│ ├── conftest.py # Shared pytest fixtures\n│ ├── unit/ # Unit tests\n│ └── integration/ # Integration tests\n├── pyproject.toml # Project configuration and dependencies\n├── install.sh # Installation script\n├── run.sh # Server execution script\n└── README.md # Project documentation\n```\n\n### 测试\n\n使用 pytest 运行测试：\n\n```bash\n# Run all tests\npytest\n\n# Run only unit tests\npytest tests/unit/\n\n# Run only integration tests\npytest tests/integration/\n\n# Run tests with specific markers\npytest -m \"auth\" # Authentication tests\npytest -m \"core\" # Core functionality tests\n\n# Run tests with coverage reporting\npytest --cov=src\n```\n\n### 调试与检查\n\n使用附带的检查工具进行调试：\n\n```bash\n# Default stdio transport\n./inspect.sh\n\n# For SSE transport\n./inspect.sh --sse\n\n# For development mode\n./inspect.sh --dev\n```\n\n## 错误处理\n\n所有 API 操作均以以下格式返回标准化错误响应：\n\n```json\n{\n \"status\": \"error\",\n \"error_type\": \"ExceptionClassName\",\n \"message\": \"Detailed error message\"\n}\n```\n\n## 性能考虑\n\n该桥实现了几项性能优化：\n\n1. **连接池**：重用 HTTP 连接以获得更好的性能\n2. **速率限制**：防止 Taiga API 过载\n3. **重试机制**：使用指数退避算法自动重试失败的请求\n4. **会话清理**：定期清理过期会话以释放资源\n\n## 贡献\n\n欢迎贡献代码！欢迎提交 Pull 请求。\n\n1. 分叉存储库\n2. 创建你的功能分支（ `git checkout -b feature/amazing-feature` ）\n3. 安装开发依赖项（ `./install.sh --dev` ）\n4. 进行更改\n5. 运行测试（ `pytest` ）\n6. 提交您的更改（ `git commit -m 'Add some amazing feature'` ）\n7. 推送到分支（ `git push origin feature/amazing-feature` ）\n8. 打开拉取请求\n\n## 执照\n\n该项目根据 MIT 许可证获得许可 - 有关详细信息，请参阅 LICENSE 文件。\n\n## 致谢\n\n* [Taiga](https://www.taiga.io/)提供出色的项目管理平台\n* 用于标准化 AI 通信框架的[模型上下文协议 (MCP)](https://github.com/mcp-foundation/specification)\n* 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