Skip to main content
Glama
anpekesen

namegender-mcp

by anpekesen
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Gender from an email address

gender_from_email

Predict gender from an email's local part by extracting the first name; returns 'unknown' for role addresses. Optionally weight results by a country code.

Instructions

Extracts a first name from the local part of an email address and predicts its gender. Returns "unknown" when no name can be extracted — role addresses such as info@ or admin@ included.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYesEmail address.
countryNoTwo-letter ISO 3166-1 country code (TR, DE, US). When given, the answer is weighted by that country's data — the same name can have a different gender by country.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral disclosure burden. It discloses extraction from the local part, gender prediction, and the specific 'unknown' fallback for role addresses such as info@ or admin@. It does not enumerate all possible return values, but covers the main edge case.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single focused sentence, front-loaded with the primary action and followed by a relevant edge-case note. Every phrase earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter tool with full schema coverage, the description covers the main behavior, the fallback, and a notable special case. It would be slightly more complete with explicit alternate routing to siblings and an exact list of possible gender outputs, but nothing critical is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameters email and country are already documented well. The description adds context about local-part extraction but does not add significant per-parameter meaning beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Extracts a first name from the local part of an email address and predicts its gender.' It also clarifies the fallback to 'unknown', which helps distinguish it from sibling tools that operate on names or usernames.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The input context is clear: this tool takes an email address and works on its local part. However, it does not explicitly say when to use this tool instead of gender_from_name, gender_from_username, or gender_bulk, leaving that routing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools