Hosted MCP Server
Set it up in one minute
- Copy your API token from your account settings.
- Pick your tool below and paste the snippet, replacing the token placeholder.
- Restart your AI tool and ask it something like: “What is the likely gender of the name Andrea in Italy?”
Run this one command in your terminal — that is the whole setup.
claude mcp add --transport http --header "Authorization: Bearer YOUR_API_TOKEN" gender-api https://mcp.gender-api.com
Add --scope user to make Gender-API available in all your projects instead of just the current one.
Claude Desktop connects through a small npm bridge that is downloaded automatically. Add this to your config file — no manual install needed.
{
"mcpServers": {
"gender-api": {
"command": "npx",
"args": [
"-y",
"mcp-remote",
"https://mcp.gender-api.com",
"--header",
"Authorization:Bearer YOUR_API_TOKEN"
]
}
}
}
Config file location:
-
macOS
~/Library/Application Support/Claude/claude_desktop_config.json -
Windows
%APPDATA%\Claude\claude_desktop_config.json
Keep Authorization:Bearer without a space after the colon — that is how the bridge expects it.
Add this to ~/.cursor/mcp.json for all projects, or .cursor/mcp.json inside a single project.
{
"mcpServers": {
"gender-api": {
"url": "https://mcp.gender-api.com",
"headers": {
"Authorization": "Bearer YOUR_API_TOKEN"
}
}
}
}
Add this to .vscode/mcp.json in your project, or to the user-level mcp.json to enable it everywhere.
{
"servers": {
"gender-api": {
"type": "http",
"url": "https://mcp.gender-api.com",
"requestInit": {
"headers": {
"Authorization": "Bearer YOUR_API_TOKEN"
}
}
}
}
}
VS Code uses servers instead of mcpServers, and expects headers inside requestInit. The same shape works in Visual Studio and JetBrains IDEs.
Any client that supports remote MCP needs just these three values:
Endpoint: https://mcp.gender-api.com Transport: streamable-http Header: Authorization: Bearer YOUR_API_TOKEN
If your client only supports local MCP servers, use the npx mcp-remote bridge shown in the Claude Desktop tab — it works with any stdio-only client.
Use Gender-API directly inside AI tools
Our MCP Server lets MCP-compatible AI tools call Gender-API directly. That means your assistant can enrich first names, full names, email-derived names, and country-of-origin data through structured tool calls instead of prompt-only instructions.
There is nothing to install and nothing to host: point your tool at https://mcp.gender-api.com and authenticate with the same Gender-API token you already use for the API.
What customers can do with it
- Use live Gender-API data: Your AI tool can query live Gender-API results instead of inventing request formats or responses.
- Reuse your existing token: The MCP server works with the same Gender-API bearer token you already use for the API.
- Connect MCP-compatible clients: You can use it in Claude, Cursor, or your own MCP-compatible internal tools and agents that support hosted MCP URLs.
- Keep billing simple: Normal Gender-API credit usage still applies, so there is no second billing system to learn.
Supported tools
Included MCP tools
- query_first_name – Query likely gender by first name.
- query_full_name – Query likely gender by full name.
- query_email – Extract and enrich a name from an email address.
- get_country_of_origin – Get likely countries of origin.
- get_statistics – Check remaining credits and account statistics.
Hosted transport
Use the hosted Streamable HTTP endpoint at https://mcp.gender-api.com and authenticate with your existing Gender-API bearer token.
Real-world MCP use cases
Lead enrichment inside AI copilots
Let Claude, Cursor, or an internal sales agent look at a lead list, call Gender-API through MCP, and add likely first names, gender, and country-of-origin context before outreach.
CRM and support personalization
Your support assistant can inspect incoming email addresses, extract likely first names with `query_email`, and create more natural personalized draft replies for agents.
Spreadsheet cleanup and research agents
MCP-enabled data-cleaning agents can review CSV files or spreadsheets, standardize first-name columns, and enrich rows with Gender-API results without custom API code in every workflow.
Marketing automation and segmentation
Use MCP in campaign-building assistants to generate salutations, enrich contact data, and check remaining credits before launching a personalization workflow.
Building your own MCP client?
Connect with the official MCP SDK
If you are writing your own agent instead of using a ready-made tool, connect the Streamable HTTP transport directly:
import { Client } from '@modelcontextprotocol/sdk/client/index.js';
import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp.js';
const transport = new StreamableHTTPClientTransport(new URL('https://mcp.gender-api.com'), {
requestInit: {
headers: {
Authorization: 'Bearer YOUR_API_TOKEN',
},
},
});
Frequently Asked Questions
Does this use my normal Gender-API credits?
Yes. The MCP server is a thin integration layer over the existing Gender-API endpoints, so normal token permissions and credit usage still apply.
Can I use it in my own internal MCP tool?
Yes. If your tool supports hosted MCP, connect it to https://mcp.gender-api.com and send your Gender-API bearer token.
Do I need to install anything?
No. The MCP server is hosted by us, so there is nothing to download or keep running. Clients that only support local MCP servers use the npx mcp-remote bridge, which downloads itself on first start.
My AI tool does not show the Gender-API tools. What now?
Restart the tool after editing its config, and double-check that you replaced the token placeholder with your real API token. If the connection is rejected, your token is invalid or out of credits — both show up as an authentication error.
Have any questions?
