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genderize

by io.github.pipeworx-io

Predict Gender Country

predict_gender_country
Read-onlyIdempotent

Predict gender from a first name in a specific country (e.g., "US", "FR", "DE"). Returns gender, probability, and regional sample size.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesFirst name to predict gender for.
country_codeYesISO 3166-1 alpha-2 country code (e.g. "US", "GB", "DE") to localize the prediction.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe name that was analyzed
genderYesPredicted gender (male, female, or null if uncertain)
countryNoISO country code used for region-specific prediction
probabilityYesConfidence probability from 0 to 1
sample_sizeYesNumber of samples used in prediction

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "country": {
+      "description": "ISO country code used for region-specific prediction",
+      "type": "string"
+    },
+    "gender": {
+      "description": "Predicted gender (male, female, or null if uncertain)",
+      "enum": [
+        "male",
+        "female",
+        null
+      ],
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "name": {
+      "description": "The name that was analyzed",
+      "type": "string"
+    },
+    "probability": {
+      "description": "Confidence probability from 0 to 1",
+      "type": "number"
+    },
+    "sample_size": {
+      "description": "Number of samples used in prediction",
+      "type": "number"
+    }
+  },
+  "required": [
+    "name",
+    "gender",
+    "probability",
+    "sample_size"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "country_code": "US",
+    "name": "Alexander"
+  },
+  {
+    "country_code": "DE",
+    "name": "Andrea"
+  }
+]
  3. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive behavior. The description adds value by stating the return fields (gender, probability, regional sample size) and confirming the country-specific scope, which are not covered by annotations.

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 that front-loads the core purpose, followed by output details. Every word contributes value with no redundancy.

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

Completeness5/5

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

Given the simple two-parameter schema, rich output schema, and strong annotations, the description fully covers the tool's behavior and return values. No critical information is missing for an agent to select and invoke it correctly.

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 coverage is 100% with clear descriptions for both 'name' and 'country_code'. The description provides sample country codes but does not add semantic meaning beyond the schema, so baseline 3 is appropriate.

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 clearly states a specific action ('Predict gender from a first name') and a specific resource ('in a specific country'), with examples. This distinguishes it from the sibling tool predict_gender, which likely lacks country-specific context.

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

Usage Guidelines4/5

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

The phrase 'in a specific country' implies the tool is for country-aware predictions, which is useful context. However, it does not explicitly mention alternatives like the simpler predict_gender tool or state when not to use this variant, so it lacks explicit exclusions.

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

All genderize toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tools route the same style of query to the same Pipeworx catalog: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap in purpose, and ask_pipeworx_beta is explicitly identical to ask_pipeworx. The prediction-market tools also blur together, with bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, and polymarket_fill_risk all covering overlapping analysis territory.

Naming Consistency3/5

The tools are consistently lowercase snake_case, but the naming convention is mixed: some are verb_noun (predict_gender, generate_llms_txt), some are noun phrases (entity_profile, recent_alerts), some are bare verbs (remember, forget), and many share domain prefixes like ask_pipeworx or polymarket_. It is readable, but there is no single predictable pattern across the set.

Tool Count2/5

At 33 tools, this exceeds the 25+ threshold where the surface becomes hard to navigate. More importantly, the count does not match the server's apparent genderize identity: the vast majority of tools are unrelated Pipeworx research, prediction-market, memory, and subscription utilities bolted onto a two-tool gender-prediction core.

Completeness3/5

As a broad research assistant, the set is substantial: it covers question routing, grounded verification, deep research, entity profiles, comparisons, change feeds, memory, and subscriptions. However, the actual genderize domain is thin—just two prediction tools with no batch, supported-country, or accuracy endpoints—and several unrelated capabilities feel bolted on, making coverage uneven.

Resources