IPGeolocation.io

@IPGeolocation.io look up geolocation data for 203.0.113.42, 198.51.100.7, and example.com — I need city, country, and ISP for each.

Bulk geolocation returned three results in order: 203.0.113.42 resolves to Sydney, Australia (AS1234, Example ISP); 198.51.100.7 maps to Toronto, Canada (AS5678, Sample Networks); example.com's current IP is in Ashburn, Virginia, US (AS9012, Cloud Provider Inc.). Each entry includes latitude/longitude, timezone (AEDT, EST, EST respectively), and currency codes. All three consumed 3 API credits from your plan.

Bulk lookup preserves input order and handles mixed IPs and domains. Each valid item costs 1 credit; optional fields like security or currency data may increase cost. Domain resolution reflects current DNS state, not historical records.

@IPGeolocation.io convert 2024-01-15 14:30 from America/New_York to Asia/Tokyo and tell me the local time plus UTC offset for both zones.

Conversion complete: 2024-01-15 14:30 EST (UTC-5) in New York equals 2024-01-16 04:30 JST (UTC+9) in Tokyo. The 14-hour difference accounts for the date rollover. Both timezones were on standard time in mid-January; daylight saving adjustments would shift offsets in summer months. This operation consumed 1 API credit.

Timezone conversion handles DST transitions automatically and works with IANA identifiers, coordinates, or airport codes. Costs 1 credit per successful conversion. Useful for scheduling across regions but requires explicit timezone names—ambiguous city names may fail.

@IPGeolocation.io get astronomy data for 40.7128,-74.0060 on 2024-06-21 and summarize sunrise, sunset, and moon phase for that summer solstice in New York.

For June 21, 2024 at 40.7128°N, 74.0060°W (New York City): sunrise at 05:24 EDT, sunset at 20:30 EDT—yielding 15 hours 6 minutes of daylight, the year's longest. Moon phase was waning gibbous at 92% illumination, rising at 22:47. Solar noon occurred at 12:57 with the sun reaching 73.5° altitude. Golden hour and twilight windows included for photography planning. Request cost 1 API credit.

Astronomy lookup provides Sun/Moon events, twilight phases, and positional data for any coordinate and date. Ideal for planning outdoor shoots or understanding seasonal light patterns. The time-series variant covers up to 90 days in one request for the same 1-credit cost.

When bulk IP security checks beat per-transaction lookups

A 6-person e-commerce team processing 200-400 orders per day needs to flag suspicious transactions before fulfillment. The Bulk Look Up IP Security tool wins here because it batches up to 50 addresses in one request—your order-processing queue can group IPs every 30 seconds and run threat checks at 2 credits per address instead of making 400 individual calls. The ASN lookup adds context when a flagged IP comes from a known proxy farm. This setup breaks down above 1,000 orders per day unless you move the batch job to off-peak hours; at that scale you want a dedicated fraud service. If your checkout already logs IPs and you run nightly reconciliation, this MCP turns a 6-hour manual review into a 10-minute Switchy workflow.

Geolocation for regional feature gates and compliance

A 3-person product team shipping a B2B SaaS app needs to enforce GDPR data residency and show region-specific pricing. The Bulk Look Up IP Geolocation tool handles login-time checks for up to 50 concurrent users in one call, returning country, region, and timezone data at 1 credit per IP. You wire this into your auth middleware so each session knows which data center to route to and which terms-of-service variant to display. The timezone conversion tool keeps meeting-scheduler features accurate across your user base. This approach works cleanly until you hit 500+ daily active users—then you cache geolocation results by IP range and refresh weekly. If your app already has a CDN doing geo-routing, you don't need this MCP; if you're rolling your own regional logic, it saves you from maintaining a local MaxMind database.

Parsing user-agents to prioritize mobile-specific bugs

A 4-person support team triaging 80-120 tickets per week sees recurring issues that only affect certain browsers or mobile OS versions. The Bulk Parse User-Agents tool processes up to 50 user-agent strings in one request at 1 credit each, so your morning standup workflow in Switchy can tag tickets with device, OS, and browser metadata before you assign them. This surfaces patterns like "all Safari 17 users on iPad report the same checkout error" without manually decoding cryptic UA strings. The astronomy tools are irrelevant here unless your app has location-based features tied to sunrise or moon phase. This setup pays off when you have enough ticket volume to justify batch processing; below 30 tickets per week, you're better off with a browser-extension parser. If your ticketing system already extracts device data, skip this MCP.