[{"_1":2,"_336":-5,"_337":-5},"loaderData",{"_3":4,"_22":23,"_24":25,"_273":274},"root",{"_5":6,"_7":8},"toasts",[],"visitor",{"_9":10,"_11":12},"viewportWidth",1280,"visitorSession",{"_13":14,"_16":17,"_18":-5,"_19":20,"_21":-5},"attributes",[15],"BOT","id",6512954,"membership","referenceId","0196d6cb-548b-75ab-9354-e237a3417f0c","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_26":27,"_79":-5,"_80":40,"_29":81,"_190":191,"_272":137},"availableLocales",[28,40,46,51,58,65,72],{"_29":30,"_33":37,"_38":39},"mcpServer",{"_31":32,"_33":34,"_35":36},"description","Ein Server, der es KI-Agenten ermöglicht, über einen DynamoDB-gestützten FastAPI-Dienst Informationen zu bevorstehenden Veranstaltungen im Nachtclub Berghain abzufragen und abzurufen.","name","Berghain Events MCP Server","readme","# Berghain Events API & MCP-Implementierung\n\n## Projektstruktur\n\n```\nberghain-api/\n├── app/ # Aplicación principal FastAPI\n│ ├── api/ # Rutas de la API\n│ ├── core/ # Configuración y componentes centrales\n│ ├── db/ # Capa de acceso a datos (DynamoDB)\n│ └── main.py # Punto de entrada de la aplicación y FastMCP server\n├── scripts/ # Scripts para creación de tabla y carga de datos en DynamoDB\n│ ├── create_table.py\n│ └── load_data.py\n├── events/ # Directorio para los JSON extraídos de Firecrawl\n├── Dockerfile # Dockerfile para despliegue\n├── requirements.txt # Dependencias del proyecto (para uv)\n└── README.md # Este archivo\n```\n\n## Anforderungen\n\n* Python 3.10+\n* uv (für Abhängigkeitsmanagement und virtuelle Umgebung)\n* AWS-Konto (für DynamoDB und App Runner)\n* Firecrawl-API-Schlüssel\n\n## Einrichtung\n\n1. Klonen Sie dieses Repository:\n\n ```bash\n git clone \n cd berghain-api\n ```\n\n2. Erstellen und aktivieren Sie eine virtuelle Umgebung mit `uv` :\n\n ```bash\n uv venv\n source .venv/bin/activate # En Linux/macOS\n # .venv\\\\Scripts\\\\activate # En Windows\n ```\n\n3. Installieren Sie Abhängigkeiten mit `uv` :\n\n ```bash\n uv pip install -r requirements.txt\n ```\n\n## Detaillierter Prozess\n\n### 1. Datenextraktion mit Firecrawl\n\n* **Firecrawl MCP auf Cursor einstellen:** Stellen Sie sicher, dass Sie Ihren Firecrawl-API-Schlüssel haben. Fügen Sie in der Cursor-MCP-Konfiguration Folgendes hinzu:\n\n ```json\n \"firecrawl-mcp\": {\n \"command\": \"npx\",\n \"args\": [\n \"-y\",\n \"firecrawl-mcp\"\n ],\n \"env\": {\n \"FIRECRAWL_API_KEY\": \"fc-YOUR_FIRECRAWL_API_KEY\"\n }\n }\n ```\n\n* **Führen Sie die Extraktion aus:** Verwenden Sie einen Agenten in Cursor (oder einem ähnlichen Tool), um das Firecrawl MCP aufzurufen und es aufzufordern, Ereignisse von `https://www.berghain.berlin/en/program/` zu extrahieren.\n\n* **Daten speichern:** Die extrahierten Daten sollten als JSON-Dateien im Verzeichnis `events/` gespeichert werden. Beispiel: `events/berghain_events_YYYY-MM-DD.json` .\n\n### 2. Daten in DynamoDB laden\n\n* **Erstellen Sie die Tabelle in DynamoDB:** Das Skript `scripts/create_table.py` kümmert sich darum. Führen Sie es aus (passen Sie die Parameter gegebenenfalls an):\n\n ```bash\n uv run python scripts/create_table.py --table berghain_events --region tu-region-aws\n # Para desarrollo local con DynamoDB Local (ej. docker run -p 8000:8000 amazon/dynamodb-local):\n # uv run python scripts/create_table.py --table berghain_events --region localhost --endpoint-url http://localhost:8000\n ```\n\n* **Daten in die Tabelle laden:** Das Skript `scripts/load_data.py` lädt Ereignisse aus JSON-Dateien.\n\n ```bash\n uv run python scripts/load_data.py --table berghain_events --region tu-region-aws --path events\n # Para desarrollo local:\n # uv run python scripts/load_data.py --table berghain_events --region localhost --endpoint-url http://localhost:8000 --path events\n ```\n\n Stellen Sie sicher, dass `app/core/config.py` (importiert von `load_data.py` ) über die erforderlichen Konfigurationen verfügt, wenn Sie diese nicht als Argumente übergeben.\n\n### 3. API mit FastAPI und MCP Server\n\n* Die API-Logik befindet sich im Verzeichnis `app/` , wobei die Endpunkte definiert sind (z. B. in `app/api/endpoints/events.py` ).\n* Die Datei `app/main.py` ist so konfiguriert, dass die FastAPI-Anwendung und der FastMCP-Server gestartet werden und die API-Endpunkte als Tools für KI-Modelle verfügbar gemacht werden. Überprüfen Sie die `custom_maps` in `app/main.py` um zu sehen, wie GET-Routen `RouteType.TOOL` zugeordnet werden.\n\n## Bereitstellung auf AWS\n\n### Zu. `Dockerfile`\n\nStellen Sie sicher, dass Ihr `Dockerfile` richtig für die Verwendung `uv` konfiguriert ist und führen Sie `app/main.py` aus:\n\n### B. Erstellen und Hochladen eines Images in Amazon ECR (Elastic Container Registry)\n\n1. **Docker mit ECR authentifizieren:**\n\n ```bash\n aws ecr get-login-password --region tu-region-aws | docker login --username AWS --password-stdin tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com\n ```\n\n2. **Erstellen Sie ein Repository in ECR (falls es nicht vorhanden ist):**\n\n ```bash\n aws ecr create-repository --repository-name berghain-mcp-api --region tu-region-aws\n ```\n\n3. **Erstellen Sie Ihr Docker-Image:**\n\n ```bash\n docker build -t berghain-mcp-api .\n ```\n\n4. **Markieren Sie Ihr Bild:**\n\n ```bash\n docker tag berghain-mcp-api:latest tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com/berghain-mcp-api:latest\n ```\n\n5. **Laden Sie das Bild zu ECR hoch:**\n\n ```bash\n docker push tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com/berghain-mcp-api:latest\n ```\n\n Ersetzen Sie `tu-region-aws` und `tu-aws-account-id` durch Ihre Werte.\n\n### C. Bereitstellen der Infrastruktur mit Terraform\n\n1. **Bereiten Sie Ihre Terraform-Dateien vor:** Stellen Sie sicher, dass Sie Ihre Terraform-Konfigurationsdateien (z. B. `main.tf` , `variables.tf` , `outputs.tf` ) in einem Verzeichnis (z. B. `terraform/` ) haben. Diese Dateien sollten die erforderlichen AWS-Ressourcen definieren, z. B. den AWS App Runner-Dienst, den das ECR-Image verwenden wird, und die DynamoDB-Tabelle (sofern sie auch von Terraform verwaltet wird). Ihre App Runner-Konfiguration in Terraform muss auf das in ECR hochgeladene Bild verweisen.\n\n2. **Navigieren Sie zum Terraform-Verzeichnis:**\n\n ```bash\n cd terraform \n ```\n\n3. **Terraform initialisieren:**\n\n ```bash\n terraform init\n ```\n\n4. **Wenden Sie die Terraform-Konfiguration an:**\n\n ```bash\n terraform apply\n ```\n\n Überprüfen Sie den Plan und bestätigen Sie die Anwendung. Terraform stellt die Ressourcen bereit.\n\n5. **Holen Sie sich die Service-URL:** Nach der Anwendung sollte Terraform die definierten Ausgaben anzeigen, einschließlich der App Runner-Service-URL. Notieren Sie sich diese URL (z. B. `https://..awsapprunner.com` ).\n\n## Testen der bereitgestellten Lösung\n\n### Zu. Lokales Testskript anpassen ( `mcp_local.py` )\n\nLegen Sie die Datei `mcp_local.py` im Stammverzeichnis Ihres Projekts fest\n\n**Wichtig:** Aktualisieren Sie die Variable `mcp_server_url` in `mcp_local.py` mit der URL, die Sie aus der Terraform-Ausgabe erhalten haben.\n\n### B. Test ausführen\n\nVom Stammverzeichnis Ihres Projekts (oder wo auch immer Sie `mcp_local.py` gespeichert haben):\n\n```bash\nuv run python mcp_local.py\n```\n\nDadurch wird der PydanticAI-Agent ausgeführt, der versucht, eine Verbindung zu Ihrem bereitgestellten MCP herzustellen und die Abfrage auszuführen.\n\n## Lizenz\n\nMIT","German","tag","de-DE",{"_29":41,"_33":44,"_38":45},{"_31":42,"_33":34,"_35":43},"A server that allows AI agents to query and retrieve information about upcoming events at Berghain nightclub through a DynamoDB-backed FastAPI service.","# Berghain Events API & MCP Implementation\n\n## Project structure\n\n```\nberghain-api/\n├── app/ # Aplicación principal FastAPI\n│ ├── api/ # Rutas de la API\n│ ├── core/ # Configuración y componentes centrales\n│ ├── db/ # Capa de acceso a datos (DynamoDB)\n│ └── main.py # Punto de entrada de la aplicación y FastMCP server\n├── scripts/ # Scripts para creación de tabla y carga de datos en DynamoDB\n│ ├── create_table.py\n│ └── load_data.py\n├── events/ # Directorio para los JSON extraídos de Firecrawl\n├── Dockerfile # Dockerfile para despliegue\n├── requirements.txt # Dependencias del proyecto (para uv)\n└── README.md # Este archivo\n```\n\n## Requirements\n\n* Python 3.10+\n* uv (for dependency management and virtual environment)\n* AWS Account (for DynamoDB and App Runner)\n* Firecrawl API Key\n\n## Facility\n\n1. Clone this repository:\n\n ```bash\n git clone \n cd berghain-api\n ```\n\n2. Create and activate a virtual environment with `uv` :\n\n ```bash\n uv venv\n source .venv/bin/activate # En Linux/macOS\n # .venv\\\\Scripts\\\\activate # En Windows\n ```\n\n3. Install dependencies with `uv` :\n\n ```bash\n uv pip install -r requirements.txt\n ```\n\n## Detailed Process\n\n### 1. Data Extraction with Firecrawl\n\n* **Configure the Firecrawl MCP in Cursor:** Make sure you have your Firecrawl API Key. In the Cursor MCP settings, add:\n\n ```json\n \"firecrawl-mcp\": {\n \"command\": \"npx\",\n \"args\": [\n \"-y\",\n \"firecrawl-mcp\"\n ],\n \"env\": {\n \"FIRECRAWL_API_KEY\": \"fc-YOUR_FIRECRAWL_API_KEY\"\n }\n }\n ```\n\n* **Run the extraction:** Use an agent in Cursor (or a similar tool) to call the Firecrawl MCP and ask it to extract events from `https://www.berghain.berlin/en/program/` .\n\n* **Save the data:** The extracted data should be saved as JSON files in the `events/` directory. For example, `events/berghain_events_YYYY-MM-DD.json` .\n\n### 2. Loading Data into DynamoDB\n\n* **Create the table in DynamoDB:** The `scripts/create_table.py` script takes care of this. Run it (adjust the parameters if necessary):\n\n ```bash\n uv run python scripts/create_table.py --table berghain_events --region tu-region-aws\n # Para desarrollo local con DynamoDB Local (ej. docker run -p 8000:8000 amazon/dynamodb-local):\n # uv run python scripts/create_table.py --table berghain_events --region localhost --endpoint-url http://localhost:8000\n ```\n\n* **Load data into the table:** The `scripts/load_data.py` script loads events from JSON files.\n\n ```bash\n uv run python scripts/load_data.py --table berghain_events --region tu-region-aws --path events\n # Para desarrollo local:\n # uv run python scripts/load_data.py --table berghain_events --region localhost --endpoint-url http://localhost:8000 --path events\n ```\n\n Make sure that `app/core/config.py` (imported by `load_data.py` ) has the necessary configurations if you don't pass them as arguments.\n\n### 3. API with FastAPI and MCP Server\n\n* The API logic resides in the `app/` directory, with the endpoints defined (e.g., in `app/api/endpoints/events.py` ).\n* The `app/main.py` file is configured to launch the FastAPI application and the FastMCP server, exposing the API endpoints as tools for AI models. Review the `custom_maps` in `app/main.py` to see how GET routes are mapped to `RouteType.TOOL` .\n\n## Deployment on AWS\n\n### a. `Dockerfile`\n\nMake sure your `Dockerfile` is configured correctly to use `uv` and run `app/main.py` :\n\n### b. Build and Upload Image to Amazon ECR (Elastic Container Registry)\n\n1. **Authenticate Docker with ECR:**\n\n ```bash\n aws ecr get-login-password --region tu-region-aws | docker login --username AWS --password-stdin tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com\n ```\n\n2. **Create a repository in ECR (if it doesn't exist):**\n\n ```bash\n aws ecr create-repository --repository-name berghain-mcp-api --region tu-region-aws\n ```\n\n3. **Build your Docker image:**\n\n ```bash\n docker build -t berghain-mcp-api .\n ```\n\n4. **Tag your image:**\n\n ```bash\n docker tag berghain-mcp-api:latest tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com/berghain-mcp-api:latest\n ```\n\n5. **Upload the image to ECR:**\n\n ```bash\n docker push tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com/berghain-mcp-api:latest\n ```\n\n Replace `tu-region-aws` and `tu-aws-account-id` with your values.\n\n### c. Deploy Infrastructure with Terraform\n\n1. **Prepare your Terraform files:** Make sure you have your Terraform configuration files (e.g., `main.tf` , `variables.tf` , `outputs.tf` ) in a directory (e.g., `terraform/` ). These files should define the necessary AWS resources, such as the AWS App Runner service that will use the ECR image, and the DynamoDB table (if it is also managed by Terraform). Your App Runner configuration in Terraform should reference the uploaded ECR image.\n\n2. **Navigate to the Terraform directory:**\n\n ```bash\n cd terraform \n ```\n\n3. **Initialize Terraform:**\n\n ```bash\n terraform init\n ```\n\n4. **Apply the Terraform configuration:**\n\n ```bash\n terraform apply\n ```\n\n Review the plan and confirm the application. Terraform will provision the resources.\n\n5. **Get the service URL:** Once applied, Terraform should display the defined outputs, including the App Runner service URL. Make a note of this URL (e.g., `https://..awsapprunner.com` ).\n\n## Test the Deployed Solution\n\n### a. Adjust Local Test Script ( `mcp_local.py` )\n\nSet the `mcp_local.py` file to the root of your project\n\n**Important:** Update the `mcp_server_url` variable in `mcp_local.py` with the URL you got from the Terraform output.\n\n### b. Run Test\n\nFrom the root of your project (or wherever you saved `mcp_local.py` ):\n\n```bash\nuv run python mcp_local.py\n```\n\nThis will run the PydanticAI agent, which will attempt to connect to your deployed MCP and perform the query.\n\n## License\n\nMIT","English","en-US",{"_29":47,"_33":49,"_38":50},{"_31":42,"_33":34,"_35":48},"# Berghain Events API & MCP Implementation\n\n\n## Estructura del proyecto\n\n```\nberghain-api/\n├── app/ # Aplicación principal FastAPI\n│ ├── api/ # Rutas de la API\n│ ├── core/ # Configuración y componentes centrales\n│ ├── db/ # Capa de acceso a datos (DynamoDB)\n│ └── main.py # Punto de entrada de la aplicación y FastMCP server\n├── scripts/ # Scripts para creación de tabla y carga de datos en DynamoDB\n│ ├── create_table.py\n│ └── load_data.py\n├── events/ # Directorio para los JSON extraídos de Firecrawl\n├── Dockerfile # Dockerfile para despliegue\n├── requirements.txt # Dependencias del proyecto (para uv)\n└── README.md # Este archivo\n```\n\n## Requisitos\n\n- Python 3.10+\n- uv (para gestión de dependencias y entorno virtual)\n- Cuenta de AWS (para DynamoDB y App Runner)\n- API Key de Firecrawl\n\n## Instalación\n\n1. Clonar este repositorio:\n ```bash\n git clone \n cd berghain-api\n ```\n2. Crear y activar un entorno virtual con `uv`:\n ```bash\n uv venv\n source .venv/bin/activate # En Linux/macOS\n # .venv\\\\Scripts\\\\activate # En Windows\n ```\n3. Instalar dependencias con `uv`:\n ```bash\n uv pip install -r requirements.txt\n ```\n\n## Proceso Detallado\n\n### 1. Extracción de Datos con Firecrawl\n\n- **Configura el MCP de Firecrawl en Cursor:**\n Asegúrate de tener tu API Key de Firecrawl. En la configuración de MCPs de Cursor, añade:\n ```json\n \"firecrawl-mcp\": {\n \"command\": \"npx\",\n \"args\": [\n \"-y\",\n \"firecrawl-mcp\"\n ],\n \"env\": {\n \"FIRECRAWL_API_KEY\": \"fc-YOUR_FIRECRAWL_API_KEY\"\n }\n }\n ```\n- **Ejecuta la extracción:**\n Usa un agente en Cursor (o una herramienta similar) para llamar al MCP de Firecrawl y pedirle que extraiga los eventos de `https://www.berghain.berlin/en/program/`.\n- **Guarda los datos:**\n Los datos extraídos deben guardarse como archivos JSON en el directorio `events/`. Por ejemplo, `events/berghain_events_YYYY-MM-DD.json`.\n\n### 2. Carga de Datos a DynamoDB\n\n- **Crea la tabla en DynamoDB:**\n El script `scripts/create_table.py` se encarga de esto. Ejecútalo (ajusta los parámetros si es necesario):\n ```bash\n uv run python scripts/create_table.py --table berghain_events --region tu-region-aws\n # Para desarrollo local con DynamoDB Local (ej. docker run -p 8000:8000 amazon/dynamodb-local):\n # uv run python scripts/create_table.py --table berghain_events --region localhost --endpoint-url http://localhost:8000\n ```\n- **Carga los datos a la tabla:**\n El script `scripts/load_data.py` sube los eventos desde los archivos JSON.\n ```bash\n uv run python scripts/load_data.py --table berghain_events --region tu-region-aws --path events\n # Para desarrollo local:\n # uv run python scripts/load_data.py --table berghain_events --region localhost --endpoint-url http://localhost:8000 --path events\n ```\n Asegúrate de que `app/core/config.py` (importado por `load_data.py`) tenga las configuraciones necesarias si no las pasas como argumentos.\n\n### 3. API con FastAPI y Servidor MCP\n\n- La lógica de la API reside en el directorio `app/`, con los endpoints definidos (e.g., en `app/api/endpoints/events.py`).\n- El archivo `app/main.py` está configurado para iniciar la aplicación FastAPI y el servidor FastMCP, exponiendo los endpoints de la API como tools para modelos de IA. Revisa las `custom_maps` en `app/main.py` para ver cómo se mapean las rutas GET a `RouteType.TOOL`.\n\n## Despliegue en AWS\n\n### a. `Dockerfile`\n\nAsegúrate de que tu `Dockerfile` esté configurado correctamente para usar `uv` y ejecutar `app/main.py`:\n\n\n\n### b. Construir y Subir Imagen a Amazon ECR (Elastic Container Registry)\n\n1. **Autentica Docker con ECR:**\n ```bash\n aws ecr get-login-password --region tu-region-aws | docker login --username AWS --password-stdin tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com\n ```\n2. **Crea un repositorio en ECR (si no existe):**\n ```bash\n aws ecr create-repository --repository-name berghain-mcp-api --region tu-region-aws\n ```\n3. **Construye tu imagen Docker:**\n ```bash\n docker build -t berghain-mcp-api .\n ```\n4. **Etiqueta tu imagen:**\n ```bash\n docker tag berghain-mcp-api:latest tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com/berghain-mcp-api:latest\n ```\n5. **Sube la imagen a ECR:**\n ```bash\n docker push tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com/berghain-mcp-api:latest\n ```\n Reemplaza `tu-region-aws` y `tu-aws-account-id` con tus valores.\n\n### c. Desplegar Infraestructura con Terraform\n\n1. **Prepara tus archivos de Terraform:**\n Asegúrate de tener tus archivos de configuración de Terraform (ej. `main.tf`, `variables.tf`, `outputs.tf`) en un directorio (p.ej., `terraform/`). Estos archivos deben definir los recursos de AWS necesarios, como el servicio de AWS App Runner que usará la imagen de ECR, y la tabla DynamoDB (si Terraform también la gestiona).\n Tu configuración de App Runner en Terraform deberá referenciar la imagen subida a ECR.\n2. **Navega al directorio de Terraform:**\n ```bash\n cd terraform \n ```\n3. **Inicializa Terraform:**\n ```bash\n terraform init\n ```\n4. **Aplica la configuración de Terraform:**\n ```bash\n terraform apply\n ```\n Revisa el plan y confirma la aplicación. Terraform provisionará los recursos.\n5. **Obtén la URL del servicio:**\n Una vez aplicado, Terraform debería mostrar las salidas definidas, incluyendo la URL del servicio App Runner. Anota esta URL (ej. `https://..awsapprunner.com`).\n\n## Probar la Solución Desplegada\n\n### a. Ajustar Script de Prueba Local (`mcp_local.py`)\n\nAjusta el archivo `mcp_local.py` en la raíz de tu proyecto\n\n**Importante:** Actualiza la variable `mcp_server_url` en `mcp_local.py` con la URL que obtuviste del output de Terraform.\n\n### b. Ejecutar Prueba\n\nDesde la raíz de tu proyecto (o donde hayas guardado `mcp_local.py`):\n```bash\nuv run python mcp_local.py\n```\nEsto ejecutará el agente PydanticAI, que intentará conectarse a tu MCP desplegado y realizar la consulta.\n\n\n\n## Licencia\n\nMIT\n\n","Spanish","es-ES",{"_29":52,"_33":56,"_38":57},{"_31":53,"_33":54,"_35":55},"DynamoDB を利用した FastAPI サービスを通じて、AI エージェントが Berghain ナイトクラブの今後のイベントに関する情報を照会および取得できるようにするサーバー。","ベルグハインイベントMCPサーバー","# ベルグハインイベントAPIとMCP実装\n\n## プロジェクト構造\n\n```\nberghain-api/\n├── app/ # Aplicación principal FastAPI\n│ ├── api/ # Rutas de la API\n│ ├── core/ # Configuración y componentes centrales\n│ ├── db/ # Capa de acceso a datos (DynamoDB)\n│ └── main.py # Punto de entrada de la aplicación y FastMCP server\n├── scripts/ # Scripts para creación de tabla y carga de datos en DynamoDB\n│ ├── create_table.py\n│ └── load_data.py\n├── events/ # Directorio para los JSON extraídos de Firecrawl\n├── Dockerfile # Dockerfile para despliegue\n├── requirements.txt # Dependencias del proyecto (para uv)\n└── README.md # Este archivo\n```\n\n## 要件\n\n* Python 3.10以上\n* uv (依存関係管理と仮想環境用)\n* AWS アカウント (DynamoDB および App Runner 用)\n* Firecrawl APIキー\n\n## 施設\n\n1. このリポジトリをクローンします:\n\n ```bash\n git clone \n cd berghain-api\n ```\n\n2. `uv`を使用して仮想環境を作成し、アクティブ化します。\n\n ```bash\n uv venv\n source .venv/bin/activate # En Linux/macOS\n # .venv\\\\Scripts\\\\activate # En Windows\n ```\n\n3. `uv`を使用して依存関係をインストールします。\n\n ```bash\n uv pip install -r requirements.txt\n ```\n\n## 詳細なプロセス\n\n### 1. Firecrawlによるデータ抽出\n\n* **Firecrawl MCP をカーソルに設定します。Firecrawl** API キーがあることを確認します。カーソル MCP 構成で、以下を追加します。\n\n ```json\n \"firecrawl-mcp\": {\n \"command\": \"npx\",\n \"args\": [\n \"-y\",\n \"firecrawl-mcp\"\n ],\n \"env\": {\n \"FIRECRAWL_API_KEY\": \"fc-YOUR_FIRECRAWL_API_KEY\"\n }\n }\n ```\n\n* **抽出を実行します。Cursor**のエージェント (または同様のツール) を使用して、Firecrawl MCP を呼び出し、 `https://www.berghain.berlin/en/program/`からイベントを抽出するように要求します。\n\n* \\*\\*データを保存する:\\*\\*抽出されたデータは、 `events/`ディレクトリに JSON ファイルとして保存する必要があります。たとえば、 `events/berghain_events_YYYY-MM-DD.json` 。\n\n### 2. DynamoDBへのデータのロード\n\n* **DynamoDB にテーブルを作成します。scripts** `scripts/create_table.py`スクリプトがこれを処理します。実行します（必要に応じてパラメータを調整します）。\n\n ```bash\n uv run python scripts/create_table.py --table berghain_events --region tu-region-aws\n # Para desarrollo local con DynamoDB Local (ej. docker run -p 8000:8000 amazon/dynamodb-local):\n # uv run python scripts/create_table.py --table berghain_events --region localhost --endpoint-url http://localhost:8000\n ```\n\n* **テーブルにデータをロードします。scripts** `scripts/load_data.py`スクリプトは、JSON ファイルからイベントをロードします。\n\n ```bash\n uv run python scripts/load_data.py --table berghain_events --region tu-region-aws --path events\n # Para desarrollo local:\n # uv run python scripts/load_data.py --table berghain_events --region localhost --endpoint-url http://localhost:8000 --path events\n ```\n\n 引数として渡さない場合は`app/core/config.py` ( `load_data.py`によってインポート) に必要な構成があることを確認してください。\n\n### 3. FastAPIとMCPサーバーを使用したAPI\n\n* API ロジックは`app/`ディレクトリにあり、エンドポイントが定義されています (例: `app/api/endpoints/events.py` )。\n* `app/main.py`ファイルは、FastAPI アプリケーションと FastMCP サーバーを起動するように構成され、API エンドポイントを AI モデルのツールとして公開します。 `app/main.py`の`custom_maps`をチェックして、 GET ルートが`RouteType.TOOL`にどのようにマップされるかを確認します。\n\n## AWS へのデプロイメント\n\n### に。 `Dockerfile`\n\n`uv`を使用し、 `app/main.py`を実行するように`Dockerfile`が正しく構成されていることを確認します。\n\n### b.イメージをビルドして Amazon ECR (Elastic Container Registry) にアップロードする\n\n1. **ECR を使用して Docker を認証します。**\n\n ```bash\n aws ecr get-login-password --region tu-region-aws | docker login --username AWS --password-stdin tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com\n ```\n\n2. **ECR にリポジトリを作成します (存在しない場合)。**\n\n ```bash\n aws ecr create-repository --repository-name berghain-mcp-api --region tu-region-aws\n ```\n\n3. **Docker イメージをビルドします。**\n\n ```bash\n docker build -t berghain-mcp-api .\n ```\n\n4. **画像にタグを付けます:**\n\n ```bash\n docker tag berghain-mcp-api:latest tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com/berghain-mcp-api:latest\n ```\n\n5. **画像を ECR にアップロードします。**\n\n ```bash\n docker push tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com/berghain-mcp-api:latest\n ```\n\n `tu-region-aws`と`tu-aws-account-id`実際の値に置き換えます。\n\n### 紀元前Terraform でインフラストラクチャをデプロイする\n\n1. **Terraform ファイルを準備します。Terraform**構成ファイル (例: `main.tf` 、 `variables.tf` 、 `outputs.tf` ) がディレクトリ (例: `terraform/` ) にあることを確認します。これらのファイルでは、ECR イメージが使用する AWS App Runner サービスや DynamoDB テーブル (Terraform でも管理されている場合) などの必要な AWS リソースを定義する必要があります。 Terraform の App Runner 構成では、ECR にアップロードされたイメージを参照する必要があります。\n\n2. **Terraform ディレクトリに移動します。**\n\n ```bash\n cd terraform \n ```\n\n3. **Terraform を初期化します。**\n\n ```bash\n terraform init\n ```\n\n4. **Terraform 構成を適用します。**\n\n ```bash\n terraform apply\n ```\n\n 計画を確認し、申請を確定します。 Terraform がリソースをプロビジョニングします。\n\n5. \\*\\*サービス URL を取得します。\\*\\*適用すると、Terraform によって、App Runner サービス URL を含む定義済みの出力が表示されます。この URL (例: `https://..awsapprunner.com` ) をメモします。\n\n## 展開したソリューションをテストする\n\n### に。ローカルテストスクリプトを調整する ( `mcp_local.py` )\n\n`mcp_local.py`ファイルをプロジェクトのルートに設定します。\n\n**重要:** `mcp_local.py`の`mcp_server_url`変数を、Terraform 出力から取得した URL に更新します。\n\n### b.テストを実行\n\nプロジェクトのルート (または`mcp_local.py`保存した場所) から:\n\n```bash\nuv run python mcp_local.py\n```\n\nこれにより、PydanticAI エージェントが実行され、デプロイされた MCP に接続してクエリを実行しようとします。\n\n## ライセンス\n\nマサチューセッツ工科大学","Japanese","ja-JP",{"_29":59,"_33":63,"_38":64},{"_31":60,"_33":61,"_35":62},"DynamoDB 기반 FastAPI 서비스를 통해 AI 에이전트가 Berghain 나이트클럽에서 열리는 다가올 이벤트에 대한 정보를 쿼리하고 검색할 수 있는 서버입니다.","Berghain 이벤트 MCP 서버","# Berghain 이벤트 API 및 MCP 구현\n\n## 프로젝트 구조\n\n지엑스피1\n\n## 요구 사항\n\n* 파이썬 3.10+\n* uv(종속성 관리 및 가상 환경용)\n* AWS 계정(DynamoDB 및 App Runner용)\n* Firecrawl API 키\n\n## 시설\n\n1. 이 저장소를 복제하세요:\n\n ```bash\n git clone \n cd berghain-api\n ```\n\n2. `uv` 사용하여 가상 환경을 만들고 활성화합니다.\n\n ```bash\n uv venv\n source .venv/bin/activate # En Linux/macOS\n # .venv\\\\Scripts\\\\activate # En Windows\n ```\n\n3. `uv` 로 종속성을 설치합니다:\n\n ```bash\n uv pip install -r requirements.txt\n ```\n\n## 세부 프로세스\n\n### 1. Firecrawl을 이용한 데이터 추출\n\n* **Firecrawl MCP를 커서로 설정:** Firecrawl API 키가 있는지 확인하세요. 커서 MCP 구성에서 다음을 추가합니다.\n\n ```json\n \"firecrawl-mcp\": {\n \"command\": \"npx\",\n \"args\": [\n \"-y\",\n \"firecrawl-mcp\"\n ],\n \"env\": {\n \"FIRECRAWL_API_KEY\": \"fc-YOUR_FIRECRAWL_API_KEY\"\n }\n }\n ```\n\n* **추출을 실행합니다.** Cursor의 에이전트(또는 비슷한 도구)를 사용하여 Firecrawl MCP를 호출하고 `https://www.berghain.berlin/en/program/` 에서 이벤트를 추출하도록 요청합니다.\n\n* **데이터 저장:** 추출된 데이터는 `events/` 디렉토리에 JSON 파일로 저장해야 합니다. 예를 들어, `events/berghain_events_YYYY-MM-DD.json` .\n\n### 2. DynamoDB에 데이터 로드\n\n* **DynamoDB에 테이블을 생성합니다.** `scripts/create_table.py` 스크립트가 이 작업을 처리합니다. 실행합니다(필요한 경우 매개변수를 조정합니다):\n\n ```bash\n uv run python scripts/create_table.py --table berghain_events --region tu-region-aws\n # Para desarrollo local con DynamoDB Local (ej. docker run -p 8000:8000 amazon/dynamodb-local):\n # uv run python scripts/create_table.py --table berghain_events --region localhost --endpoint-url http://localhost:8000\n ```\n\n* **테이블에 데이터 로드:** `scripts/load_data.py` 스크립트는 JSON 파일에서 이벤트를 로드합니다.\n\n ```bash\n uv run python scripts/load_data.py --table berghain_events --region tu-region-aws --path events\n # Para desarrollo local:\n # uv run python scripts/load_data.py --table berghain_events --region localhost --endpoint-url http://localhost:8000 --path events\n ```\n\n 인수로 전달하지 않는 경우, `load_data.py` 에서 가져온 `app/core/config.py` 에 필요한 구성이 있는지 확인하세요.\n\n### 3. FastAPI 및 MCP 서버를 사용한 API\n\n* API 로직은 `app/` 디렉토리에 있으며 엔드포인트는 정의되어 있습니다(예: `app/api/endpoints/events.py` ).\n* `app/main.py` 파일은 FastAPI 애플리케이션과 FastMCP 서버를 시작하도록 구성되어 API 엔드포인트를 AI 모델을 위한 도구로 노출합니다. `app/main.py` 의 `custom_maps` 확인하여 GET 경로가 `RouteType.TOOL` 에 어떻게 매핑되는지 확인하세요.\n\n## AWS에 배포\n\n### 에게. `Dockerfile`\n\n`Dockerfile` 이 `uv` 사용하도록 올바르게 구성되었고 `app/main.py` 실행했는지 확인하세요.\n\n### 비. Amazon ECR(Elastic Container Registry)에 이미지 빌드 및 업로드\n\n1. **ECR로 Docker 인증:**\n\n ```bash\n aws ecr get-login-password --region tu-region-aws | docker login --username AWS --password-stdin tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com\n ```\n\n2. **ECR에 저장소를 만듭니다(존재하지 않는 경우):**\n\n ```bash\n aws ecr create-repository --repository-name berghain-mcp-api --region tu-region-aws\n ```\n\n3. **Docker 이미지를 빌드하세요.**\n\n ```bash\n docker build -t berghain-mcp-api .\n ```\n\n4. **이미지에 태그를 지정하세요:**\n\n ```bash\n docker tag berghain-mcp-api:latest tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com/berghain-mcp-api:latest\n ```\n\n5. **ECR에 이미지 업로드:**\n\n ```bash\n docker push tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com/berghain-mcp-api:latest\n ```\n\n `tu-region-aws` 과 `tu-aws-account-id` 원하는 값으로 바꾸세요.\n\n### 기음. Terraform을 사용하여 인프라 배포\n\n1. **Terraform 파일을 준비합니다.** Terraform 구성 파일(예: `main.tf` , `variables.tf` , `outputs.tf` )이 디렉토리(예: `terraform/` )에 있는지 확인합니다. 이러한 파일은 ECR 이미지가 사용할 AWS App Runner 서비스, DynamoDB 테이블(Terraform에서 관리하는 경우) 등 필요한 AWS 리소스를 정의해야 합니다. Terraform의 App Runner 구성은 ECR에 업로드된 이미지를 참조해야 합니다.\n\n2. **Terraform 디렉토리로 이동합니다.**\n\n ```bash\n cd terraform \n ```\n\n3. **Terraform 초기화:**\n\n ```bash\n terraform init\n ```\n\n4. **Terraform 구성을 적용합니다.**\n\n ```bash\n terraform apply\n ```\n\n 계획을 검토하고 신청을 확인하세요. Terraform이 리소스를 프로비저닝합니다.\n\n5. **서비스 URL 가져오기:** 적용되면 Terraform은 App Runner 서비스 URL을 포함한 정의된 출력을 표시해야 합니다. 이 URL을 기록해 두세요(예 `https://..awsapprunner.com` ).\n\n## 배포된 솔루션 테스트\n\n### 에게. 로컬 테스트 스크립트 조정( `mcp_local.py` )\n\n`mcp_local.py` 파일을 프로젝트의 루트로 설정하세요.\n\n**중요:** `mcp_local.py` 의 `mcp_server_url` 변수를 Terraform 출력에서 얻은 URL로 업데이트합니다.\n\n### 비. 테스트 실행\n\n프로젝트의 루트(또는 `mcp_local.py` 저장한 곳)에서:\n\n```bash\nuv run python mcp_local.py\n```\n\n이렇게 하면 PydanticAI 에이전트가 실행되어 배포된 MCP에 연결하고 쿼리를 수행합니다.\n\n## 특허\n\nMIT","Korean","ko-KR",{"_29":66,"_33":70,"_38":71},{"_31":67,"_33":68,"_35":69},"Сервер, который позволяет агентам ИИ запрашивать и извлекать информацию о предстоящих событиях в ночном клубе Berghain через службу FastAPI, поддерживаемую DynamoDB.","MCP-сервер Berghain Events","# API событий Berghain и реализация MCP\n\n## Структура проекта\n\n```\nberghain-api/\n├── app/ # Aplicación principal FastAPI\n│ ├── api/ # Rutas de la API\n│ ├── core/ # Configuración y componentes centrales\n│ ├── db/ # Capa de acceso a datos (DynamoDB)\n│ └── main.py # Punto de entrada de la aplicación y FastMCP server\n├── scripts/ # Scripts para creación de tabla y carga de datos en DynamoDB\n│ ├── create_table.py\n│ └── load_data.py\n├── events/ # Directorio para los JSON extraídos de Firecrawl\n├── Dockerfile # Dockerfile para despliegue\n├── requirements.txt # Dependencias del proyecto (para uv)\n└── README.md # Este archivo\n```\n\n## Требования\n\n* Питон 3.10+\n* uv (для управления зависимостями и виртуальной средой)\n* Аккаунт AWS (для DynamoDB и App Runner)\n* API-ключ Firecrawl\n\n## Средство\n\n1. Клонируйте этот репозиторий:\n\n ```bash\n git clone \n cd berghain-api\n ```\n\n2. Создайте и активируйте виртуальную среду с помощью `uv` :\n\n ```bash\n uv venv\n source .venv/bin/activate # En Linux/macOS\n # .venv\\\\Scripts\\\\activate # En Windows\n ```\n\n3. Установите зависимости с помощью `uv` :\n\n ```bash\n uv pip install -r requirements.txt\n ```\n\n## Подробный процесс\n\n### 1. Извлечение данных с помощью Firecrawl\n\n* **Установите Firecrawl MCP на курсор:** убедитесь, что у вас есть ключ API Firecrawl. В конфигурации Cursor MCPs добавьте:\n\n ```json\n \"firecrawl-mcp\": {\n \"command\": \"npx\",\n \"args\": [\n \"-y\",\n \"firecrawl-mcp\"\n ],\n \"env\": {\n \"FIRECRAWL_API_KEY\": \"fc-YOUR_FIRECRAWL_API_KEY\"\n }\n }\n ```\n\n* **Запустите извлечение:** используйте агент в Cursor (или аналогичный инструмент), чтобы вызвать Firecrawl MCP и попросить его извлечь события из `https://www.berghain.berlin/en/program/` .\n\n* **Сохраните данные:** Извлеченные данные следует сохранить в виде файлов JSON в каталоге `events/` . Например, `events/berghain_events_YYYY-MM-DD.json` .\n\n### 2. Загрузка данных в DynamoDB\n\n* **Создайте таблицу в DynamoDB:** этим занимается скрипт `scripts/create_table.py` . Запустите его (при необходимости настройте параметры):\n\n ```bash\n uv run python scripts/create_table.py --table berghain_events --region tu-region-aws\n # Para desarrollo local con DynamoDB Local (ej. docker run -p 8000:8000 amazon/dynamodb-local):\n # uv run python scripts/create_table.py --table berghain_events --region localhost --endpoint-url http://localhost:8000\n ```\n\n* **Загрузка данных в таблицу:** Скрипт `scripts/load_data.py` загружает события из файлов JSON.\n\n ```bash\n uv run python scripts/load_data.py --table berghain_events --region tu-region-aws --path events\n # Para desarrollo local:\n # uv run python scripts/load_data.py --table berghain_events --region localhost --endpoint-url http://localhost:8000 --path events\n ```\n\n Убедитесь, что `app/core/config.py` (импортированный `load_data.py` ) имеет необходимые конфигурации, если вы не передаете их в качестве аргументов.\n\n### 3. API с FastAPI и MCP Server\n\n* Логика API находится в каталоге `app/` , а конечные точки определены (например, в `app/api/endpoints/events.py` ).\n* Файл `app/main.py` настроен на запуск приложения FastAPI и сервера FastMCP, предоставляя конечные точки API в качестве инструментов для моделей ИИ. Проверьте `custom_maps` в `app/main.py` чтобы увидеть, как маршруты GET сопоставляются с `RouteType.TOOL` .\n\n## Развертывание на AWS\n\n### к. `Dockerfile`\n\nУбедитесь, что ваш `Dockerfile` правильно настроен для использования `uv` и запуска `app/main.py` :\n\n### б. Создание и загрузка образа в Amazon ECR (Elastic Container Registry)\n\n1. **Аутентификация Docker с помощью ECR:**\n\n ```bash\n aws ecr get-login-password --region tu-region-aws | docker login --username AWS --password-stdin tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com\n ```\n\n2. **Создайте репозиторий в ECR (если его нет):**\n\n ```bash\n aws ecr create-repository --repository-name berghain-mcp-api --region tu-region-aws\n ```\n\n3. **Создайте свой образ Docker:**\n\n ```bash\n docker build -t berghain-mcp-api .\n ```\n\n4. **Отметьте свое изображение:**\n\n ```bash\n docker tag berghain-mcp-api:latest tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com/berghain-mcp-api:latest\n ```\n\n5. **Загрузите изображение в ECR:**\n\n ```bash\n docker push tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com/berghain-mcp-api:latest\n ```\n\n Замените `tu-region-aws` и `tu-aws-account-id` своими значениями.\n\n### в. Развертывание инфраструктуры с помощью Terraform\n\n1. **Подготовьте файлы Terraform:** убедитесь, что файлы конфигурации Terraform (например, `main.tf` , `variables.tf` , `outputs.tf` ) находятся в каталоге (например, `terraform/` ). Эти файлы должны определять необходимые ресурсы AWS, такие как служба AWS App Runner, которую будет использовать образ ECR, и таблица DynamoDB (если Terraform также управляет ею). Ваша конфигурация App Runner в Terraform должна ссылаться на изображение, загруженное в ECR.\n\n2. **Перейдите в каталог Terraform:**\n\n ```bash\n cd terraform \n ```\n\n3. **Инициализация Terraform:**\n\n ```bash\n terraform init\n ```\n\n4. **Примените конфигурацию Terraform:**\n\n ```bash\n terraform apply\n ```\n\n Ознакомьтесь с планом и подтвердите заявку. Terraform предоставит ресурсы.\n\n5. **Получите URL-адрес службы:** после применения Terraform должен отобразить определенные выходные данные, включая URL-адрес службы App Runner. Запишите этот URL-адрес (например `https://..awsapprunner.com` ).\n\n## Протестируйте развернутое решение\n\n### к. Настройте локальный тестовый скрипт ( `mcp_local.py` )\n\nУстановите файл `mcp_local.py` в корневой каталог вашего проекта.\n\n**Важно:** обновите переменную `mcp_server_url` в `mcp_local.py` , указав URL-адрес, полученный из выходных данных Terraform.\n\n### б. Выполнить тест\n\nИз корня вашего проекта (или там, где вы сохранили `mcp_local.py` ):\n\n```bash\nuv run python mcp_local.py\n```\n\nЭто запустит агент PydanticAI, который попытается подключиться к развернутому MCP и выполнить запрос.\n\n## Лицензия\n\nМассачусетский технологический институт","Russian","ru-RU",{"_29":73,"_33":77,"_38":78},{"_31":74,"_33":75,"_35":76},"该服务器允许 AI 代理通过 DynamoDB 支持的 FastAPI 服务查询和检索有关 Berghain 夜总会即将举行的活动的信息。","Berghain 活动 MCP 服务器","# Berghain 活动 API 和 MCP 实施\n\n## 项目结构\n\n```\nberghain-api/\n├── app/ # Aplicación principal FastAPI\n│ ├── api/ # Rutas de la API\n│ ├── core/ # Configuración y componentes centrales\n│ ├── db/ # Capa de acceso a datos (DynamoDB)\n│ └── main.py # Punto de entrada de la aplicación y FastMCP server\n├── scripts/ # Scripts para creación de tabla y carga de datos en DynamoDB\n│ ├── create_table.py\n│ └── load_data.py\n├── events/ # Directorio para los JSON extraídos de Firecrawl\n├── Dockerfile # Dockerfile para despliegue\n├── requirements.txt # Dependencias del proyecto (para uv)\n└── README.md # Este archivo\n```\n\n## 要求\n\n* Python 3.10+\n* uv（用于依赖管理和虚拟环境）\n* AWS 账户（用于 DynamoDB 和 App Runner）\n* Firecrawl API 密钥\n\n## 设施\n\n1. 克隆此存储库：\n\n ```bash\n git clone \n cd berghain-api\n ```\n\n2. 使用`uv`创建并激活虚拟环境：\n\n ```bash\n uv venv\n source .venv/bin/activate # En Linux/macOS\n # .venv\\\\Scripts\\\\activate # En Windows\n ```\n\n3. 使用`uv`安装依赖项：\n\n ```bash\n uv pip install -r requirements.txt\n ```\n\n## 详细流程\n\n### 1. 使用 Firecrawl 提取数据\n\n* \\*\\*将 Firecrawl MCP 设置为 Cursor：\\*\\*确保您拥有 Firecrawl API 密钥。在 Cursor MCPs 配置中，添加：\n\n ```json\n \"firecrawl-mcp\": {\n \"command\": \"npx\",\n \"args\": [\n \"-y\",\n \"firecrawl-mcp\"\n ],\n \"env\": {\n \"FIRECRAWL_API_KEY\": \"fc-YOUR_FIRECRAWL_API_KEY\"\n }\n }\n ```\n\n* \\*\\*运行提取：\\*\\*使用 Cursor 中的代理（或类似工具）调用 Firecrawl MCP 并要求它从`https://www.berghain.berlin/en/program/`中提取事件。\n\n* \\*\\*保存数据：\\*\\*提取的数据应保存为`events/`目录中的 JSON 文件。例如， `events/berghain_events_YYYY-MM-DD.json` 。\n\n### 2.将数据加载到 DynamoDB\n\n* **在 DynamoDB 中创建表：** `scripts/create_table.py`脚本负责处理此事。运行它（如有必要，调整参数）：\n\n ```bash\n uv run python scripts/create_table.py --table berghain_events --region tu-region-aws\n # Para desarrollo local con DynamoDB Local (ej. docker run -p 8000:8000 amazon/dynamodb-local):\n # uv run python scripts/create_table.py --table berghain_events --region localhost --endpoint-url http://localhost:8000\n ```\n\n* **将数据加载到表中：** `scripts/load_data.py`脚本从 JSON 文件加载事件。\n\n ```bash\n uv run python scripts/load_data.py --table berghain_events --region tu-region-aws --path events\n # Para desarrollo local:\n # uv run python scripts/load_data.py --table berghain_events --region localhost --endpoint-url http://localhost:8000 --path events\n ```\n\n 如果您不将配置作为参数传递，请确保`app/core/config.py` （由`load_data.py`导入）具有必要的配置。\n\n### 3. 使用 FastAPI 和 MCP 服务器的 API\n\n* API 逻辑位于`app/`目录中，并定义了端点（例如，在`app/api/endpoints/events.py`中）。\n* `app/main.py`文件配置为启动 FastAPI 应用程序和 FastMCP 服务器，将 API 端点公开为 AI 模型的工具。检查`app/main.py`中的`custom_maps`以查看 GET 路由如何映射到`RouteType.TOOL` 。\n\n## 在 AWS 上部署\n\n### 到。 `Dockerfile`\n\n确保您的`Dockerfile`已正确配置以使用`uv`并运行`app/main.py` ：\n\n### b.构建并上传图像至 Amazon ECR（弹性容器注册表）\n\n1. **使用 ECR 对 Docker 进行身份验证：**\n\n ```bash\n aws ecr get-login-password --region tu-region-aws | docker login --username AWS --password-stdin tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com\n ```\n\n2. **在 ECR 中创建一个存储库（如果不存在）：**\n\n ```bash\n aws ecr create-repository --repository-name berghain-mcp-api --region tu-region-aws\n ```\n\n3. **构建你的 Docker 镜像：**\n\n ```bash\n docker build -t berghain-mcp-api .\n ```\n\n4. **标记您的图像：**\n\n ```bash\n docker tag berghain-mcp-api:latest tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com/berghain-mcp-api:latest\n ```\n\n5. **将图像上传到 ECR：**\n\n ```bash\n docker push tu-aws-account-id.dkr.ecr.tu-region-aws.amazonaws.com/berghain-mcp-api:latest\n ```\n\n 将`tu-region-aws`和`tu-aws-account-id`替换为您的值。\n\n### c.使用 Terraform 部署基础设施\n\n1. \\*\\*准备您的 Terraform 文件：\\*\\*确保您的 Terraform 配置文件（例如`main.tf` 、 `variables.tf` 、 `outputs.tf` ）位于目录（例如`terraform/` ）中。这些文件应该定义必要的 AWS 资源，例如 ECR 映像将使用的 AWS App Runner 服务，以及 DynamoDB 表（如果 Terraform 也管理它）。 Terraform 中的 App Runner 配置将需要引用上传到 ECR 的图像。\n\n2. **导航到 Terraform 目录：**\n\n ```bash\n cd terraform \n ```\n\n3. **初始化 Terraform：**\n\n ```bash\n terraform init\n ```\n\n4. **应用 Terraform 配置：**\n\n ```bash\n terraform apply\n ```\n\n 审查计划并确认申请。 Terraform 将提供资源。\n\n5. \\*\\*获取服务 URL：\\*\\*一旦应用，Terraform 应该显示定义的输出，包括 App Runner 服务 URL。记下此 URL（例如`https://..awsapprunner.com` ）。\n\n## 测试已部署的解决方案\n\n### 到。调整本地测试脚本（ `mcp_local.py` ）\n\n将`mcp_local.py`文件设置为项目的根目录\n\n\\*\\*重要提示：\\*\\*使用您从 Terraform 输出中获得的 URL 更新`mcp_local.py`中的`mcp_server_url`变量。\n\n### b.运行测试\n\n从项目的根目录（或保存`mcp_local.py`任何位置）：\n\n```bash\nuv run python mcp_local.py\n```\n\n这将运行 PydanticAI 代理，它将尝试连接到您部署的 MCP 并执行查询。\n\n## 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