anpekesen/namegender-mcp
by Various
Model Context Protocol server for the NameGender API: gender from names, emails and usernames, with probability, sample size and source on every answer.
MCP
anpekesen/namegender-mcp
Added 8 Oct 2026
Overview
Model Context Protocol server that wraps the NameGender API to infer gender from names, emails, and usernames. Every result includes a probability score, sample size, and source. Written in JavaScript for MCP-compatible clients.
Best for
Best for
Developers who need quick name-based gender guesses inside MCP-compatible AI tools.
Use cases
- Enrich CRM records with gender predictions
- Resolve gender from user emails in chatbot flows
- Add gender context to lead scoring pipelines
How to use
Install
claude mcp add namegender -e NAMEGENDER_API_KEY=ng_live_... -- npx -y namegender-mcp Tools exposed
gender_from_namegender_from_emailgender_from_usernamegender_bulkname_countriesaccount_statusNAMEGENDER_API_KEYNAMEGENDER_BASE_URL
Tested with
Claude Desktop, Claude Code, Cursor
Example client config
{\n "mcpServers": {\n "namegender": {\n "command": "npx",\n "args": ["-y", "namegender-mcp"],\n "env": { "NAMEGENDER_API_KEY": "ng_live_..." }\n }\n }\n} Notes
Model Context Protocol server that wraps the NameGender API to infer gender from names, emails, and usernames. Every result includes a probability score, sample size, and source. Written in JavaScript for MCP-compatible clients.
0 stars on GitHub. Last updated 2026-09-21. Licensed MIT.
Use cases
- Enrich CRM records with gender predictions
- Resolve gender from user emails in chatbot flows
- Add gender context to lead scoring pipelines
Pros
- Works across names, emails, and usernames
- Provides probability, sample size, and source for transparency
- Simple MCP integration for JavaScript projects
Cons
- No stars or community adoption yet
- Depends on an external API, adding a third-party call
- Name-based inference is not a reliable measure of gender identity
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Works across names, emails, and usernames
- Provides probability, sample size, and source for transparency
- Simple MCP integration for JavaScript projects
Cons
- No stars or community adoption yet
- Depends on an external API, adding a third-party call
- Name-based inference is not a reliable measure of gender identity
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