Gender API

@gender-api check the gender distribution for these first names: Alex, Jordan, Taylor, Morgan, and Casey

Based on Gender API's database, here's the gender distribution for those names: Alex is 68% male / 32% female (high confidence), Jordan is 54% male / 46% female (moderate confidence), Taylor is 51% female / 49% male (low confidence), Morgan is 58% female / 42% male (moderate confidence), and Casey is 52% male / 48% female (low confidence). All five names show significant gender ambiguity in English-speaking regions, which may affect classification accuracy for individual cases.

This example shows the MCP's core name-to-gender lookup with probability scores. Gender API returns confidence levels based on historical data, but these are statistical patterns—not deterministic facts. The API consumes one credit per name query, so batch requests like this will use 5 credits from your account balance.

@gender-api what's my current credit balance and how many lookups have I used this month?

Your Gender API account currently has 8,247 credits remaining out of your 10,000 monthly allocation. You've performed 1,753 lookups this billing period, which started on January 1st. At your current usage rate (approximately 58 lookups per day), you'll have sufficient credits through month-end. Your account tier allows 10,000 requests per month with a rate limit of 100 requests per minute.

This example demonstrates the statistics tool, which helps you monitor API quota before running large batch operations. The MCP requires a valid API key with active subscription status. This is a read-only operation that doesn't consume credits, making it useful for pre-flight checks in automated workflows.

@gender-api analyze this CSV of customer emails and tell me the likely gender breakdown for our user base, then suggest localization priorities

I've processed 342 email addresses from your CSV using Gender API's email-based inference. The breakdown is approximately 58% male, 39% female, and 3% indeterminate (emails with no recognizable name patterns). Top countries of origin detected: United States (41%), United Kingdom (18%), Germany (12%), France (9%). For localization priorities, I'd recommend focusing on English (US/UK), German, and French based on this name-origin data. Note that email-based gender detection has lower confidence than first-name lookups since it relies on extracting name fragments from email addresses.

This example pairs the MCP's email-query tool with AI reasoning to generate strategic insights. The MCP processes each email individually (consuming one credit per address), so a 342-row CSV uses 342 credits. Email-based detection is less accurate than direct name lookups because email formats vary widely—some users have initials, nicknames, or non-name identifiers.

When Gender API fits cold-email personalization at scale

A 6-person sales team running outbound campaigns needs to personalize email greetings without manually researching 200+ leads per week. Gender API's email and full-name tools let Switchy enrich a CSV of prospects in one prompt, returning likely gender for salutation logic. The API key auth means no OAuth dance, and the statistics tool surfaces credit burn before you hit a limit mid-batch. This works when your lead list is Western-skewed and you're comfortable with probabilistic inference—accuracy drops on non-Latin names or gender-neutral contexts. If your outreach spans 50+ countries or you need audit-grade certainty, manual review or a localized data partner is safer. For fast, good-enough personalization on English-market lists, Gender API closes the loop in Switchy without leaving the workspace.

Why Gender API struggles with research ethics and nuance

A 3-person UX team wants to segment interview transcripts by participant demographics, including gender inferred from names in scheduling emails. Gender API can technically return a guess from the email tool, but this scenario hits two walls: the API returns binary male/female predictions with no non-binary or prefer-not-to-say handling, and inferring gender without consent violates most research IRB standards. Even if your study allows inference, the country-of-origin tool's probabilistic output introduces bias when names don't map cleanly to Western datasets. If you're categorizing participants, collect self-reported gender in your screener form and store it in a structured field Switchy can reference. Gender API isn't built for research rigor—it's built for marketing heuristics. Skip this MCP and design your intake to respect participant identity from the start.

When Gender API speeds up badge and email prep for small events

A 2-person event ops team is printing name badges and drafting welcome emails for a 150-attendee conference. Registrants submitted first names only, and the team wants gendered salutations for a formal tone. Gender API's first-name tool runs the list in Switchy, returning likely gender plus confidence scores so the team can flag ambiguous cases for manual review. The statistics tool confirms remaining credits before the batch runs, avoiding a half-processed export. This works when your event demographic skews Western, you're okay with binary gender assumptions, and you have 30 minutes to spot-check low-confidence results. If your event attracts international attendees or you want inclusive language, default to gender-neutral greetings and skip the inference step entirely. For traditional corporate events with tight timelines, Gender API gets badges printed without a spreadsheet marathon.