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Developer Toolkit

Database Design and Queries

Picture this: You’re staring at a complex database migration that needs to handle millions of records, maintain referential integrity, and minimize downtime. Traditional approaches might involve hours of manual schema analysis, query planning, and testing. With AI-powered database development through MCP servers, this becomes a collaborative conversation where Claude or Cursor helps you understand schema relationships, generate optimized queries, and plan safe migrations—all while maintaining deep context about your specific database structure.

This shift from manual database administration to AI-assisted development doesn’t just speed up individual tasks; it fundamentally changes how you approach database work, enabling more sophisticated optimizations and safer migrations while reducing the cognitive load of managing complex schemas.

The Database Development Challenge

Section titled “The Database Development Challenge”

Context Limitations

Traditional AI tools lose track of complex schema relationships and constraints when working with large databases, leading to invalid queries and broken foreign key references.

Query Complexity

Performance optimization requires deep understanding of execution plans, indexing strategies, and database-specific features that generic AI assistants often miss.

Schema Evolution

Database migrations must maintain data integrity while minimizing downtime, requiring careful planning of rollback procedures and dependency management.

Security Concerns

Database access requires careful permission management, especially when AI tools need to inspect schemas and generate queries against production systems.

The solution lies in MCP (Model Context Protocol) servers that provide AI assistants with direct, structured access to your database systems. Instead of copying and pasting schema dumps or trying to describe complex table relationships, MCP servers maintain persistent connections that allow AI tools to dynamically explore schemas, validate queries, and understand constraints.

Essential MCP Database Servers

Section titled “Essential MCP Database Servers”

Before diving into workflows, let’s establish the database MCP servers that make AI-powered database development possible. These servers act as intelligent bridges between your AI assistant and database systems, providing schema awareness, query validation, and performance insights.

Primary MCP Server: @modelcontextprotocol/server-postgres

Cursor Setup:

Terminal window
# In Cursor: Settings → MCP → Add Server
npx -y @modelcontextprotocol/server-postgres postgresql://user:password@localhost:5432/dbname

Claude Code Setup:

Terminal window
claude mcp add postgres -- npx -y @modelcontextprotocol/server-postgres \
  "postgresql://user:password@localhost:5432/dbname"

Key Features:

  • Schema introspection with constraint analysis
  • EXPLAIN plan interpretation
  • Index recommendation engine
  • Migration safety validation
  • PostGIS spatial query support

Schema Design Through Conversation

Section titled “Schema Design Through Conversation”

When working with database MCP servers, schema design becomes a collaborative dialogue rather than isolated planning. The AI assistant can examine your existing schema, understand business requirements, and propose modifications while maintaining referential integrity.

Business Domain Analysis

Section titled “Business Domain Analysis”

Let’s explore how this works with a real e-commerce scenario. Instead of designing schemas in isolation, we can have the AI assistant analyze existing data patterns and business requirements:

“I need to add a subscription model to our existing e-commerce platform. Can you analyze our current user and order tables, then design a subscription system that integrates cleanly with our existing data structure?”

The AI, with access to your schema through the MCP server, might respond by first examining your current structure, identifying key relationships, and then proposing:

-- AI examines existing schema and proposes additions
-- Based on current users table structure
CREATE TABLE subscription_plans (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(100) NOT NULL,
    price DECIMAL(10,2) NOT NULL,
    billing_cycle INTERVAL NOT NULL, -- e.g., '1 month'::interval
    features JSONB DEFAULT '{}',
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);


-- Links to existing users table
CREATE TABLE user_subscriptions (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    user_id UUID REFERENCES users(id) ON DELETE CASCADE,
    plan_id UUID REFERENCES subscription_plans(id),
    status VARCHAR(20) DEFAULT 'active',
    current_period_start TIMESTAMP NOT NULL,
    current_period_end TIMESTAMP NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,


    -- Prevent multiple active subscriptions
    CONSTRAINT unique_active_subscription
    EXCLUDE (user_id WITH =) WHERE (status = 'active')
);

What makes this powerful is that the AI assistant has full context of your existing constraints, naming conventions, and data types. It’s not generating generic schemas—it’s creating extensions that fit seamlessly into your specific database architecture.

Evolutionary Schema Development

Section titled “Evolutionary Schema Development”

Database schemas rarely emerge perfect from initial design. More often, they evolve through iterative refinement as business requirements become clearer. MCP servers enable this evolutionary approach by maintaining context across multiple conversation turns.

Consider this iterative design session:

“Our subscription model needs to handle upgrades and downgrades. How should we modify the schema to track subscription changes over time?”

With schema context maintained, the AI can propose additional tables that work with the existing design:

-- AI proposes audit trail for subscription changes
CREATE TABLE subscription_change_log (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    user_subscription_id UUID REFERENCES user_subscriptions(id),
    previous_plan_id UUID REFERENCES subscription_plans(id),
    new_plan_id UUID REFERENCES subscription_plans(id),
    change_reason VARCHAR(50), -- 'upgrade', 'downgrade', 'renewal'
    proration_amount DECIMAL(10,2) DEFAULT 0,
    effective_date TIMESTAMP NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);


-- Add index for common queries
CREATE INDEX idx_subscription_changes_user
ON subscription_change_log(user_subscription_id, created_at DESC);

The AI can also validate this design by checking for potential issues:

“Can you identify any potential problems with this subscription change tracking approach? Consider edge cases like rapid plan changes or concurrent modifications.”

Advanced Query Development

Section titled “Advanced Query Development”

With MCP servers providing direct database access, query development transforms from guesswork into informed optimization. The AI assistant can examine actual execution plans, analyze table statistics, and propose targeted improvements.

Natural Language to Optimized SQL

Section titled “Natural Language to Optimized SQL”

One of the most powerful aspects of database MCP integration is translating business requirements directly into optimized queries. Consider this scenario:

“Find our top 10 customers by revenue this quarter, but I also need to see their average order value and frequency of purchases. Exclude any test accounts or cancelled orders.”

With schema context, the AI can generate not just correct SQL, but efficient SQL:

-- AI generates with execution plan awareness
SELECT
    c.id,
    c.email,
    c.name,
    COUNT(DISTINCT o.id) as order_count,
    SUM(o.total_amount) as total_revenue,
    AVG(o.total_amount) as avg_order_value,
    DATE_TRUNC('day', MAX(o.created_at)) as last_order_date
FROM customers c
JOIN orders o ON c.id = o.customer_id
WHERE o.created_at >= DATE_TRUNC('quarter', CURRENT_DATE)
  AND o.status != 'cancelled'
  AND c.email NOT LIKE '%test%'
  AND c.is_test_account = false
GROUP BY c.id, c.email, c.name
ORDER BY total_revenue DESC
LIMIT 10;

But here’s where MCP servers truly shine—the AI can immediately validate this query:

“Can you analyze the execution plan for this query and suggest any index improvements?”

-- AI examines EXPLAIN and suggests optimizations
-- Suggested composite index based on actual query patterns
CREATE INDEX idx_orders_customer_quarter_performance
ON orders(customer_id, created_at, status)
WHERE created_at >= '2024-01-01' AND status != 'cancelled';


-- Partial index for customer filtering
CREATE INDEX idx_customers_active
ON customers(id, email, name)
WHERE is_test_account = false;

Query Performance Archaeology

Section titled “Query Performance Archaeology”

When dealing with slow queries in production, MCP servers enable detailed performance investigation. Instead of manually copying query plans and guessing at optimizations, you can work collaboratively with the AI:

“This query is taking 15 seconds to run. Can you help me understand why and fix it?”

-- Problematic query
SELECT p.*, c.name as category_name,
       AVG(r.rating) as avg_rating,
       COUNT(r.id) as review_count
FROM products p
LEFT JOIN categories c ON p.category_id = c.id
LEFT JOIN reviews r ON p.id = r.product_id
WHERE p.price BETWEEN 100 AND 500
  AND p.created_at > '2023-01-01'
GROUP BY p.id, c.name
ORDER BY avg_rating DESC, review_count DESC;

The AI can analyze the execution plan and identify specific bottlenecks:

“The execution plan shows a sequential scan on the products table and hash joins without proper indexes. Here’s the optimization strategy:”

  1. Create covering index for the WHERE clause filters
  2. Add composite index supporting the GROUP BY operation
  3. Consider partial index for the date range that’s frequently queried
  4. Rewrite query to eliminate unnecessary sorting
-- Optimized indexes
CREATE INDEX idx_products_price_date_covering
ON products(price, created_at, id, category_id)
WHERE created_at > '2023-01-01';


-- Optimized query with better join order
WITH product_stats AS (
  SELECT
    r.product_id,
    AVG(r.rating) as avg_rating,
    COUNT(*) as review_count
  FROM reviews r
  GROUP BY r.product_id
)
SELECT
  p.id, p.name, p.price, p.created_at,
  c.name as category_name,
  COALESCE(ps.avg_rating, 0) as avg_rating,
  COALESCE(ps.review_count, 0) as review_count
FROM products p
LEFT JOIN categories c ON p.category_id = c.id
LEFT JOIN product_stats ps ON p.id = ps.product_id
WHERE p.price BETWEEN 100 AND 500
  AND p.created_at > '2023-01-01'
ORDER BY ps.avg_rating DESC NULLS LAST, ps.review_count DESC NULLS LAST;

NoSQL Query Patterns

Section titled “NoSQL Query Patterns”

Database MCP servers aren’t limited to SQL databases. MongoDB MCP servers enable sophisticated aggregation pipeline development through natural language:

“I need to analyze user behavior patterns. Group users by their signup month, calculate average orders per user, and identify users who haven’t ordered in the last 90 days.”

// AI generates MongoDB aggregation pipeline
db.users.aggregate([
  {
    $lookup: {
      from: "orders",
      localField: "_id",
      foreignField: "user_id",
      as: "orders"
    }
  },
  {
    $addFields: {
      signup_month: { $dateToString: { format: "%Y-%m", date: "$created_at" } },
      order_count: { $size: "$orders" },
      last_order: { $max: "$orders.created_at" },
      days_since_last_order: {
        $divide: [
          { $subtract: [new Date(), { $max: "$orders.created_at" }] },
          86400000
        ]
      }
    }
  },
  {
    $group: {
      _id: "$signup_month",
      user_count: { $sum: 1 },
      avg_orders_per_user: { $avg: "$order_count" },
      inactive_users: {
        $sum: {
          $cond: [
            { $or: [
              { $gt: ["$days_since_last_order", 90] },
              { $eq: ["$order_count", 0] }
            ]},
            1, 0
          ]
        }
      }
    }
  },
  { $sort: { _id: -1 } }
]);

The AI can also explain index requirements for optimal performance:

// Suggested indexes for the aggregation
db.users.createIndex({ "created_at": 1 });
db.orders.createIndex({ "user_id": 1, "created_at": -1 });

Safe Database Migrations

Section titled “Safe Database Migrations”

Database migrations in production environments require careful planning, rollback strategies, and minimal downtime approaches. MCP servers enable AI assistants to understand your current schema state and plan safe migration paths.

Migration Planning with Schema Context

Section titled “Migration Planning with Schema Context”

Consider a common scenario: adding a new feature that requires schema changes to a production system with millions of records:

“We need to add user preferences to our platform. Users should be able to store notification settings, theme preferences, and privacy settings. How should we add this without impacting our existing user base?”

With schema context, the AI can analyze your current user table structure and propose a migration strategy:

-- Phase 1: Create preferences table (non-blocking)
CREATE TABLE user_preferences (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    user_id UUID REFERENCES users(id) ON DELETE CASCADE,
    category VARCHAR(50) NOT NULL, -- 'notifications', 'theme', 'privacy'
    preferences JSONB NOT NULL DEFAULT '{}',
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,


    UNIQUE(user_id, category)
);


-- Index for common queries
CREATE INDEX idx_user_preferences_lookup
ON user_preferences(user_id, category);


-- Trigger for updated_at
CREATE TRIGGER update_user_preferences_updated_at
    BEFORE UPDATE ON user_preferences
    FOR EACH ROW
    EXECUTE FUNCTION update_updated_at_column();

The AI can also plan the rollback strategy:

-- Rollback script (Phase 1 reverse)
DROP TRIGGER IF EXISTS update_user_preferences_updated_at ON user_preferences;
DROP INDEX IF EXISTS idx_user_preferences_lookup;
DROP TABLE IF EXISTS user_preferences;

Zero-Downtime Migration Patterns

Section titled “Zero-Downtime Migration Patterns”

For more complex changes like column modifications or data type changes, the AI can plan multi-phase migrations:

“We need to change the email column from VARCHAR(255) to a proper email type with domain validation. This table has 50 million users. How do we do this safely?”

  1. Add new column with proper constraints (non-blocking)
  2. Implement dual-write in application code
  3. Backfill existing data in batches during low-traffic periods
  4. Switch read queries to use new column
  5. Remove old column after validation period
-- Phase 1: Add new column (safe, no locks)
ALTER TABLE users
ADD COLUMN email_address VARCHAR(320) CHECK (email_address ~* '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$');


-- Phase 2: Backfill script (run during maintenance window)
-- AI generates batch processing to avoid long locks
DO $$
DECLARE
    batch_size INTEGER := 10000;
    processed INTEGER := 0;
    total_rows INTEGER;
BEGIN
    SELECT COUNT(*) INTO total_rows FROM users WHERE email_address IS NULL;


    WHILE processed < total_rows LOOP
        UPDATE users
        SET email_address = email
        WHERE id IN (
            SELECT id FROM users
            WHERE email_address IS NULL
            LIMIT batch_size
        );


        processed := processed + batch_size;
        RAISE NOTICE 'Processed % of % rows', processed, total_rows;


        -- Brief pause to avoid overwhelming the system
        PERFORM pg_sleep(0.1);
    END LOOP;
END $$;


-- Phase 3: Add constraints after backfill
ALTER TABLE users ALTER COLUMN email_address SET NOT NULL;
CREATE UNIQUE INDEX CONCURRENTLY idx_users_email_address ON users(email_address);


-- Phase 4: (After application code updated)
-- Drop old column
ALTER TABLE users DROP COLUMN email;
ALTER TABLE users RENAME COLUMN email_address TO email;

Data Integrity Validation

Section titled “Data Integrity Validation”

Throughout migration processes, the AI can generate validation queries to ensure data integrity:

-- Validation queries generated by AI
-- Check for duplicate emails after migration
SELECT email, COUNT(*)
FROM users
GROUP BY email
HAVING COUNT(*) > 1;


-- Verify foreign key integrity
SELECT u.id as user_id, u.email
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE u.created_at < '2024-01-01'
  AND o.user_id IS NULL
  AND NOT EXISTS (
    SELECT 1 FROM user_preferences up
    WHERE up.user_id = u.id
  );


-- Check constraint violations
SELECT COUNT(*) as invalid_emails
FROM users
WHERE email !~* '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$';

Performance Optimization Strategies

Section titled “Performance Optimization Strategies”

Database performance optimization with AI assistance goes beyond simple index recommendations. MCP servers enable deep analysis of query patterns, table statistics, and system bottlenecks to propose comprehensive optimization strategies.

Intelligent Index Management

Section titled “Intelligent Index Management”

Traditional index management often involves guesswork and broad generalizations. With MCP server access, AI assistants can analyze actual query patterns and table statistics to propose targeted indexing strategies:

“Our application is slowing down. Can you analyze our most expensive queries and recommend specific index optimizations?”

-- AI analyzes pg_stat_statements and proposes specific indexes
-- Based on actual query patterns and selectivity


-- For frequent customer lookup queries
CREATE INDEX CONCURRENTLY idx_orders_customer_status_date
ON orders(customer_id, status, created_at DESC)
WHERE status IN ('pending', 'processing', 'shipped');


-- Partial index for recent data (90% of queries)
CREATE INDEX CONCURRENTLY idx_orders_recent_high_value
ON orders(created_at DESC, total_amount DESC)
WHERE created_at > CURRENT_DATE - INTERVAL '90 days'
  AND total_amount > 100;


-- Covering index for order summary queries
CREATE INDEX CONCURRENTLY idx_orders_summary_covering
ON orders(customer_id, created_at)
INCLUDE (status, total_amount, item_count)
WHERE status != 'cancelled';

The AI can also identify redundant or unused indexes:

-- AI identifies indexes with low usage from pg_stat_user_indexes
-- Recommendations for cleanup
DROP INDEX IF EXISTS idx_users_old_email_pattern;  -- 0.1% usage
DROP INDEX IF EXISTS idx_orders_legacy_status;     -- Replaced by composite index

Query Pattern Analysis

Section titled “Query Pattern Analysis”

Beyond individual queries, MCP servers enable analysis of broader application patterns:

“Analyze our query patterns and identify opportunities for caching, denormalization, or architectural improvements.”

The AI might discover patterns like:

Pattern Analysis Results

High-Frequency Read Patterns:

  • User profile lookups (85% of total queries)
  • Order history pagination (60% of user sessions)
  • Product catalog browsing (40% with filters)

Write Patterns:

  • Order creation (5% of queries, high impact)
  • User preference updates (low frequency, high consistency requirements)

Optimization Opportunities:

  • Redis caching for user profiles (15-minute TTL)
  • Materialized views for product catalog aggregations
  • Read replicas for analytics and reporting queries

Database-Specific Optimizations

Section titled “Database-Specific Optimizations”

MCP servers understand the specific capabilities of different database systems and can propose targeted optimizations:

-- AI leverages PostgreSQL-specific features


-- Partial indexes for common WHERE clauses
CREATE INDEX idx_orders_active
ON orders(created_at, customer_id)
WHERE status IN ('pending', 'processing');


-- BRIN indexes for time-series data
CREATE INDEX idx_logs_brin_timestamp
ON application_logs USING BRIN(created_at);


-- GIN indexes for JSONB search
CREATE INDEX idx_user_preferences_gin
ON user_preferences USING GIN(preferences);


-- Expression indexes for computed values
CREATE INDEX idx_orders_monthly
ON orders(DATE_TRUNC('month', created_at));

Specialized Database Patterns

Section titled “Specialized Database Patterns”

Enterprise database development often requires specialized patterns for specific use cases. MCP servers enable AI assistants to understand these patterns and propose implementations tailored to your specific database system and requirements.

Multi-Tenant Architecture

Section titled “Multi-Tenant Architecture”

One of the most complex database design challenges is multi-tenancy. With schema context from MCP servers, AI can analyze your current structure and propose tenant isolation strategies:

“We need to convert our single-tenant application to support multiple customers. Each tenant should have complete data isolation while sharing the same codebase. What’s the best approach for our PostgreSQL setup?”

-- AI analyzes existing schema and proposes row-level security approach
-- Create tenant table
CREATE TABLE tenants (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(255) NOT NULL,
    domain VARCHAR(255) UNIQUE NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    settings JSONB DEFAULT '{}'
);


-- Add tenant_id to existing tables
ALTER TABLE users ADD COLUMN tenant_id UUID REFERENCES tenants(id);
ALTER TABLE orders ADD COLUMN tenant_id UUID REFERENCES tenants(id);
ALTER TABLE products ADD COLUMN tenant_id UUID REFERENCES tenants(id);


-- Enable row-level security
ALTER TABLE users ENABLE ROW LEVEL SECURITY;
ALTER TABLE orders ENABLE ROW LEVEL SECURITY;
ALTER TABLE products ENABLE ROW LEVEL SECURITY;


-- Create policies for automatic tenant isolation
CREATE POLICY tenant_isolation_users ON users
FOR ALL TO application_role
USING (tenant_id = current_setting('app.tenant_id')::UUID);


CREATE POLICY tenant_isolation_orders ON orders
FOR ALL TO application_role
USING (tenant_id = current_setting('app.tenant_id')::UUID);


-- Indexes optimized for tenant queries
CREATE INDEX idx_users_tenant_email ON users(tenant_id, email);
CREATE INDEX idx_orders_tenant_customer ON orders(tenant_id, customer_id, created_at);

Time-Series Data Management

Section titled “Time-Series Data Management”

For applications handling time-series data (IoT sensors, application metrics, financial data), AI can propose partitioning and archival strategies:

“We’re collecting sensor data from 10,000 devices, with readings every minute. How should we structure this for efficient queries and storage management?”

-- AI proposes time-based partitioning strategy
CREATE TABLE sensor_readings (
    id BIGSERIAL,
    device_id INTEGER NOT NULL,
    metric_type VARCHAR(50) NOT NULL,
    value DOUBLE PRECISION NOT NULL,
    recorded_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
    metadata JSONB
) PARTITION BY RANGE (recorded_at);


-- Create monthly partitions
CREATE TABLE sensor_readings_2024_01 PARTITION OF sensor_readings
FOR VALUES FROM ('2024-01-01') TO ('2024-02-01');


CREATE TABLE sensor_readings_2024_02 PARTITION OF sensor_readings
FOR VALUES FROM ('2024-02-01') TO ('2024-03-01');


-- Automatic partition creation function
CREATE OR REPLACE FUNCTION create_monthly_partition(table_name TEXT, start_date DATE)
RETURNS void AS $$
DECLARE
    partition_name TEXT;
    end_date DATE;
BEGIN
    partition_name := table_name || '_' || to_char(start_date, 'YYYY_MM');
    end_date := start_date + INTERVAL '1 month';


    EXECUTE format('CREATE TABLE %I PARTITION OF %I FOR VALUES FROM (%L) TO (%L)',
                   partition_name, table_name, start_date, end_date);


    EXECUTE format('CREATE INDEX %I ON %I (device_id, recorded_at DESC)',
                   'idx_' || partition_name || '_device_time', partition_name);
END;
$$ LANGUAGE plpgsql;


-- Automated data retention
CREATE OR REPLACE FUNCTION cleanup_old_partitions(table_name TEXT, retention_months INTEGER)
RETURNS void AS $$
DECLARE
    cutoff_date DATE := CURRENT_DATE - (retention_months || ' months')::INTERVAL;
    partition_name TEXT;
BEGIN
    FOR partition_name IN
        SELECT schemaname||'.'||tablename
        FROM pg_tables
        WHERE tablename LIKE table_name || '_%'
        AND tablename < table_name || '_' || to_char(cutoff_date, 'YYYY_MM')
    LOOP
        EXECUTE 'DROP TABLE ' || partition_name;
        RAISE NOTICE 'Dropped partition: %', partition_name;
    END LOOP;
END;
$$ LANGUAGE plpgsql;

Advanced Caching Strategies

Section titled “Advanced Caching Strategies”

MCP servers can help design sophisticated caching layers that integrate with your database structure:

“Our API response times are too slow. Can you design a Redis caching strategy that works with our PostgreSQL schema?”

The AI can analyze your query patterns and propose multi-layered caching:

-- AI identifies cacheable query patterns and proposes Redis integration


-- User profile cache (Redis hash)
-- Key: user:{user_id}
-- TTL: 15 minutes
-- Cache user basic info + preferences


-- Order history cache (Redis sorted set)
-- Key: user_orders:{user_id}
-- Score: timestamp, Value: order_id
-- TTL: 5 minutes


-- Product catalog cache (Redis hash + search index)
-- Key: product:{product_id}
-- Search: products_by_category:{category_id}
-- TTL: 1 hour


-- Real-time inventory (Redis string)
-- Key: inventory:{product_id}
-- TTL: 30 seconds
-- Updated via database triggers

Development Workflow Integration

Section titled “Development Workflow Integration”

The true power of database MCP servers emerges when they’re integrated into your development workflow, providing context-aware assistance throughout the development lifecycle.

Schema-Aware Code Generation

Section titled “Schema-Aware Code Generation”

With database schema context, AI can generate application code that perfectly matches your database structure:

“Generate a TypeScript interface and CRUD operations for our user_preferences table.”

// AI generates based on actual schema
interface UserPreference {
  id: string;
  userId: string;
  category: 'notifications' | 'theme' | 'privacy';
  preferences: {
    [key: string]: any;
  };
  createdAt: Date;
  updatedAt: Date;
}


class UserPreferenceService {
  async getPreferences(userId: string, category?: string): Promise<UserPreference[]> {
    const query = `
      SELECT id, user_id, category, preferences, created_at, updated_at
      FROM user_preferences
      WHERE user_id = $1 ${category ? 'AND category = $2' : ''}
      ORDER BY category, created_at
    `;


    const params = category ? [userId, category] : [userId];
    return this.db.query(query, params);
  }


  async updatePreferences(
    userId: string,
    category: string,
    preferences: Record<string, any>
  ): Promise<UserPreference> {
    const query = `
      INSERT INTO user_preferences (user_id, category, preferences)
      VALUES ($1, $2, $3)
      ON CONFLICT (user_id, category)
      DO UPDATE SET
        preferences = $3,
        updated_at = CURRENT_TIMESTAMP
      RETURNING *
    `;


    return this.db.queryOne(query, [userId, category, JSON.stringify(preferences)]);
  }
}

Testing Data Management

Section titled “Testing Data Management”

AI can generate realistic test data that respects your schema constraints and relationships:

“Create a test data generator that creates realistic e-commerce data with proper foreign key relationships and business logic constraints.”

-- AI generates comprehensive test data procedures
CREATE OR REPLACE FUNCTION generate_test_ecommerce_data(
    user_count INTEGER DEFAULT 1000,
    product_count INTEGER DEFAULT 500,
    order_count INTEGER DEFAULT 5000
)
RETURNS void AS $$
DECLARE
    user_ids UUID[];
    product_ids UUID[];
    i INTEGER;
BEGIN
    -- Generate users with realistic data patterns
    INSERT INTO users (email, name, created_at, is_test_account)
    SELECT
        'testuser' || generate_series || '@example.com',
        'Test User ' || generate_series,
        NOW() - (random() * INTERVAL '2 years'),
        true
    FROM generate_series(1, user_count)
    RETURNING id INTO user_ids;


    -- Generate products across categories
    INSERT INTO products (name, description, price, category_id, created_at)
    SELECT
        'Product ' || generate_series,
        'Test product description ' || generate_series,
        (random() * 1000 + 10)::DECIMAL(10,2),
        (ARRAY[1,2,3,4,5])[ceil(random() * 5)],
        NOW() - (random() * INTERVAL '1 year')
    FROM generate_series(1, product_count)
    RETURNING id INTO product_ids;


    -- Generate orders with realistic patterns
    FOR i IN 1..order_count LOOP
        INSERT INTO orders (customer_id, status, total_amount, created_at)
        VALUES (
            user_ids[ceil(random() * array_length(user_ids, 1))],
            (ARRAY['pending','processing','shipped','delivered','cancelled'])[ceil(random() * 5)],
            (random() * 500 + 20)::DECIMAL(10,2),
            NOW() - (random() * INTERVAL '18 months')
        );
    END LOOP;


    RAISE NOTICE 'Generated % users, % products, % orders', user_count, product_count, order_count;
END;
$$ LANGUAGE plpgsql;

Security and Best Practices

Section titled “Security and Best Practices”

Database security in AI-assisted development requires careful consideration of permissions, data exposure, and audit trails.

Connection Security

Section titled “Connection Security”

Always follow the principle of least privilege when configuring MCP server database connections:

Terminal window
# Create dedicated read-only user for MCP servers
CREATE USER mcp_readonly WITH PASSWORD 'secure_random_password';


# Grant minimal necessary permissions
GRANT CONNECT ON DATABASE myapp TO mcp_readonly;
GRANT USAGE ON SCHEMA public TO mcp_readonly;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO mcp_readonly;


# For specific query optimization needs, grant EXPLAIN access
GRANT SELECT ON pg_stat_statements TO mcp_readonly;
GRANT SELECT ON pg_stat_user_tables TO mcp_readonly;
GRANT SELECT ON pg_stat_user_indexes TO mcp_readonly;

Data Privacy Considerations

Section titled “Data Privacy Considerations”

When working with sensitive data, implement data masking for development environments:

-- AI can help generate data masking functions
CREATE OR REPLACE FUNCTION mask_email(email TEXT)
RETURNS TEXT AS $$
BEGIN
    RETURN substring(email from 1 for 2) || '***@' ||
           split_part(email, '@', 2);
END;
$$ LANGUAGE plpgsql;


-- Create masked views for development
CREATE VIEW users_masked AS
SELECT
    id,
    mask_email(email) as email,
    'Test User ' || id as name,
    created_at,
    updated_at
FROM users;

Database development with MCP servers transforms the traditional approach from manual schema management and query guesswork into collaborative, context-aware development. By providing AI assistants with direct access to your database structure and statistics, you enable more sophisticated optimizations, safer migrations, and more reliable applications—all while maintaining the security and performance requirements of enterprise systems.