Multi Database MCP Server

-- Insert sample data for sensor_readings (past 30 days) INSERT INTO test_data.sensor_readings (time, sensor_id, temperature, humidity, pressure, battery_level, location) SELECT timestamp '2023-01-01 00:00:00' + (i || ' hours')::interval AS time, sensor_id, 20 + 5 * random() AS temperature, -- 20-25°C 50 + 30 * random() AS humidity, -- 50-80% 1000 + 20 * random() AS pressure, -- 1000-1020 hPa 100 - i/100.0 AS battery_level, -- Decreasing from 100% (ARRAY['room', 'kitchen', 'outdoor', 'basement', 'garage'])[1 + floor(random() * 5)::int] AS location FROM generate_series(0, 720) AS i, -- 30 days hourly data generate_series(1, 5) AS sensor_id; -- 5 sensors -- Insert sample data for weather_observations (past 90 days) INSERT INTO test_data.weather_observations (time, station_id, temperature, precipitation, wind_speed, wind_direction, atmospheric_pressure) SELECT timestamp '2023-01-01 00:00:00' + (i || ' hours')::interval AS time, station_id, 15 + 15 * random() AS temperature, -- 15-30°C CASE WHEN random() < 0.3 THEN random() * 5 ELSE 0 END AS precipitation, -- 70% chance of no rain random() * 20 AS wind_speed, -- 0-20 km/h random() * 360 AS wind_direction, -- 0-360 degrees 1010 + 10 * random() AS atmospheric_pressure -- 1010-1020 hPa FROM generate_series(0, 2160) AS i, -- 90 days hourly data generate_series(1, 3) AS station_id; -- 3 weather stations -- Insert sample data for device_metrics (past 14 days at 1 minute intervals) INSERT INTO test_data.device_metrics (time, device_id, cpu_usage, memory_usage, network_in, network_out, disk_io) SELECT timestamp '2023-01-15 00:00:00' + (i || ' minutes')::interval AS time, device_id, 10 + 70 * random() AS cpu_usage, -- 10-80% 20 + 60 * random() AS memory_usage, -- 20-80% random() * 1000 AS network_in, -- 0-1000 KB/s random() * 500 AS network_out, -- 0-500 KB/s random() * 100 AS disk_io -- 0-100 MB/s FROM generate_series(0, 20160, 60) AS i, -- 14 days, every 60 minutes (for faster insertion) generate_series(1, 10) AS device_id; -- 10 devices -- Insert sample data for stock_prices (past 2 years of daily data) INSERT INTO test_data.stock_prices (time, symbol, open_price, high_price, low_price, close_price, volume) SELECT timestamp '2022-01-01 00:00:00' + (i || ' days')::interval AS time, symbol, 100 + 50 * random() AS open_price, 100 + 50 * random() + 10 AS high_price, 100 + 50 * random() - 10 AS low_price, 100 + 50 * random() AS close_price, floor(random() * 10000 + 1000) AS volume FROM generate_series(0, 730) AS i, -- 2 years of data unnest(ARRAY['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'META']) AS symbol; -- Insert sample data for multi_partition_data INSERT INTO test_data.multi_partition_data (time, device_id, region, metric_value) SELECT timestamp '2023-01-01 00:00:00' + (i || ' hours')::interval AS time, device_id, (ARRAY['us-east', 'us-west', 'eu-central', 'ap-south', 'sa-east'])[1 + floor(random() * 5)::int] AS region, random() * 100 AS metric_value FROM generate_series(0, 720) AS i, -- 30 days hourly data generate_series(1, 20) AS device_id; -- 20 devices across regions -- Insert some regular table data INSERT INTO test_data.regular_table (name, value, created_at) SELECT 'Item ' || i::text AS name, random() * 1000 AS value, timestamp '2023-01-01 00:00:00' + (i || ' hours')::interval AS created_at FROM generate_series(1, 100) AS i;