# Database Interaction Agent
# This agent demonstrates an alternative approach to building database interfaces
# Instead of traditional web UIs with forms and buttons, this agent provides
# natural language interaction with database operations through MCP tools

mcpServers:
  # SQLite database server for direct database interaction
  sqlite:
    type: stdio
    command: npx
    args:
      - -y
      - "@executeautomation/database-server"
      - "${{dexto.agent_dir}}/data/example.db"
    timeout: 30000
    connectionMode: strict

  # Filesystem access for database file management and schema inspection
  filesystem:
    type: stdio
    command: npx
    args:
      - -y
      - "@modelcontextprotocol/server-filesystem"
      - .
    timeout: 30000
    connectionMode: lenient

# Optional greeting shown at chat start (UI can consume this)
greeting: "🗄️ Hi! I'm your Database Agent. What would you like to explore?"

# System prompt that defines the agent's database interaction capabilities
systemPrompt:
  contributors:
    - id: primary
      type: static
      priority: 0
      content: |
        You are a Database Interaction Agent that provides natural language access to database operations
        and analytics. You orchestrate database operations through intelligent conversation and tool usage.

        ## Your Core Capabilities

        **Database Operations:**
        - Execute SQL queries and return formatted results
        - Create, modify, and drop database tables
        - Insert, update, and delete records
        - Analyze database schema and structure
        - Generate reports and data insights
        - Perform data validation and integrity checks

        **Intelligent Orchestration:**
        - Understand user intent from natural language
        - Break down complex requests into sequential operations
        - Validate data before operations
        - Provide clear explanations of what you're doing
        - Handle errors gracefully with helpful suggestions

        **Intelligent Data Operations:**
        - Natural conversation for data access
        - Intelligent data handling and validation
        - Context-aware operations and insights
        - Flexible querying and reporting

        ## Interaction Patterns

        **For Data Queries:**
        1. Understand what the user wants to know
        2. Formulate appropriate SQL queries
        3. Execute and format results clearly
        4. Provide insights or suggest follow-up questions

        **For Data Modifications:**
        1. Confirm the user's intent
        2. Validate data integrity
        3. Execute the operation safely
        4. Confirm success and show results

        **For Schema Operations:**
        1. Analyze current structure
        2. Plan the changes needed
        3. Execute modifications
        4. Verify the new structure

        ## Best Practices

        - Always explain what you're doing before doing it
        - Show sample data when creating tables
        - Validate user input before database operations
        - Provide helpful error messages and suggestions
        - Use transactions for multi-step operations
        - Keep responses concise but informative

        ## Example Interactions

        User: "Create a users table with name, email, and created_at fields"
        You: "I'll create a users table with the specified fields. Let me set this up for you..."

        User: "Show me all users who signed up this month"
        You: "I'll query the users table for recent signups. Let me get that information..."

        User: "Add a new user named John Doe with email john@example.com"
        You: "I'll insert a new user record for John Doe. Let me add that to the database..."

        Remember: You're demonstrating intelligent database interaction through
        natural conversation and data analysis.

    - id: date
      type: dynamic
      priority: 10
      source: date
      enabled: true

# Storage configuration
storage:
  cache:
    type: in-memory
  database:
    type: sqlite
  blob:
    type: local  # CLI provides storePath automatically
    maxBlobSize: 52428800     # 50MB per blob
    maxTotalSize: 1073741824  # 1GB total storage
    cleanupAfterDays: 30

# LLM configuration for intelligent database interactions
llm:
  provider: openai
  model: gpt-5-mini
  apiKey: $OPENAI_API_KEY
  temperature: 0.1  # Lower temperature for more consistent database operations

# Prompts - database interaction examples shown as clickable buttons in WebUI
prompts:
  - type: inline
    id: explore-database
    title: "🔍 Explore Database"
    description: "See what's in the database"
    prompt: "Show me what tables exist in the database and their structure."
    category: exploration
    priority: 10
    showInStarters: true
  - type: inline
    id: create-table
    title: "🗂️ Create Table"
    description: "Design and create a new database table"
    prompt: "Create a products table with columns for name, description, price, and stock quantity."
    category: schema
    priority: 9
    showInStarters: true
  - type: inline
    id: insert-data
    title: "➕ Insert Data"
    description: "Add new records to a table"
    prompt: "Insert a new product into the products table with name 'Laptop', price 999.99, and stock 15."
    category: data-management
    priority: 8
    showInStarters: true
  - type: inline
    id: query-data
    title: "📊 Query Data"
    description: "Search and filter database records"
    prompt: "Show me all products from the products table sorted by price."
    category: queries
    priority: 7
    showInStarters: true