MCPeasy

""" Database models for TOOL_NAME custom tool This file defines database models that your custom tool needs. When this file exists, Alembic will automatically discover it and include the models in migration generation. Replace TOOL_NAME with your actual tool name throughout this file. """ from datetime import datetime from typing import Dict, Any, Optional import uuid from sqlalchemy import DateTime, Integer, String, func, ForeignKey, Text, Boolean from sqlalchemy.dialects.postgresql import JSONB, UUID from sqlalchemy.orm import Mapped, mapped_column, relationship # Import the base class from mcpeasy core from src.models.base import Base # Import for type hints (optional) from typing import TYPE_CHECKING if TYPE_CHECKING: from src.models.client import Client class TOOLNAMEData(Base): """ Example model for storing TOOL_NAME specific data This demonstrates how to create custom models that: - Follow mcpeasy naming conventions - Link to core models (like Client) - Store tool-specific data - Support JSONB for flexible data storage """ __tablename__ = "toolname_data" # Replace with your tool name # Primary key id: Mapped[int] = mapped_column(Integer, primary_key=True) # Link to mcpeasy core client model client_id: Mapped[uuid.UUID] = mapped_column( UUID(as_uuid=True), ForeignKey("clients.id"), nullable=False ) # Tool-specific fields external_id: Mapped[Optional[str]] = mapped_column(String(255), nullable=True) status: Mapped[str] = mapped_column(String(50), default="pending", nullable=False) data: Mapped[Optional[Dict[str, Any]]] = mapped_column(JSONB, nullable=True) # Metadata created_at: Mapped[datetime] = mapped_column(DateTime, default=func.now(), nullable=False) updated_at: Mapped[datetime] = mapped_column( DateTime, default=func.now(), onupdate=func.now(), nullable=False ) # Relationship to core client model client: Mapped["Client"] = relationship("Client") def __repr__(self) -> str: return f"<TOOLNAMEData(id={self.id}, client_id={self.client_id}, status='{self.status}')>" def to_dict(self) -> Dict[str, Any]: """Convert to dictionary for API responses""" return { "id": self.id, "client_id": str(self.client_id), "external_id": self.external_id, "status": self.status, "data": self.data, "created_at": self.created_at.isoformat(), "updated_at": self.updated_at.isoformat() } class TOOLNAMELog(Base): """ Example model for logging TOOL_NAME operations This demonstrates how to create audit/logging tables for your custom tool. """ __tablename__ = "toolname_logs" # Replace with your tool name # Primary key id: Mapped[int] = mapped_column(Integer, primary_key=True) # Link to mcpeasy core client model client_id: Mapped[uuid.UUID] = mapped_column( UUID(as_uuid=True), ForeignKey("clients.id"), nullable=False ) # Link to the main data record (optional) toolname_data_id: Mapped[Optional[int]] = mapped_column( Integer, ForeignKey("toolname_data.id"), nullable=True ) # Log fields operation: Mapped[str] = mapped_column(String(100), nullable=False) success: Mapped[bool] = mapped_column(Boolean, default=True, nullable=False) error_message: Mapped[Optional[str]] = mapped_column(Text, nullable=True) request_data: Mapped[Optional[Dict[str, Any]]] = mapped_column(JSONB, nullable=True) response_data: Mapped[Optional[Dict[str, Any]]] = mapped_column(JSONB, nullable=True) # Timing execution_time_ms: Mapped[Optional[int]] = mapped_column(Integer, nullable=True) created_at: Mapped[datetime] = mapped_column(DateTime, default=func.now(), nullable=False) # Relationships client: Mapped["Client"] = relationship("Client") toolname_data: Mapped[Optional["TOOLNAMEData"]] = relationship("TOOLNAMEData") def __repr__(self) -> str: return f"<TOOLNAMELog(id={self.id}, operation='{self.operation}', success={self.success})>" # Additional example models for different use cases class TOOLNAMEConfig(Base): """ Example model for storing per-client tool configuration This can be used as an alternative or supplement to the JSONB configuration in the core tool_configurations table. """ __tablename__ = "toolname_config" id: Mapped[int] = mapped_column(Integer, primary_key=True) client_id: Mapped[uuid.UUID] = mapped_column( UUID(as_uuid=True), ForeignKey("clients.id"), nullable=False, unique=True # One config per client ) # Specific configuration fields for your tool api_endpoint: Mapped[Optional[str]] = mapped_column(String(500), nullable=True) api_key_encrypted: Mapped[Optional[str]] = mapped_column(Text, nullable=True) # Store encrypted rate_limit: Mapped[int] = mapped_column(Integer, default=100, nullable=False) webhook_url: Mapped[Optional[str]] = mapped_column(String(500), nullable=True) # Advanced settings as JSONB advanced_settings: Mapped[Optional[Dict[str, Any]]] = mapped_column(JSONB, nullable=True) # Metadata created_at: Mapped[datetime] = mapped_column(DateTime, default=func.now(), nullable=False) updated_at: Mapped[datetime] = mapped_column( DateTime, default=func.now(), onupdate=func.now(), nullable=False ) # Relationship client: Mapped["Client"] = relationship("Client") # TODO: Add your own custom models here # # Example patterns: # # 1. Data storage models (like TOOLNAMEData above) # 2. Audit/logging models (like TOOLNAMELog above) # 3. Configuration models (like TOOLNAMEConfig above) # 4. Relationship models (many-to-many tables) # 5. Cache/temporary data models # # Best practices: # - Always link to core models via ForeignKey when relevant # - Use JSONB for flexible/evolving data structures # - Include created_at/updated_at timestamps # - Add meaningful indexes for query performance # - Follow naming convention: toolname_tablename # - Include __repr__ and to_dict methods for debugging