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Glama

genderize

by io.github.pipeworx-io

Predict Gender

predict_gender
Read-onlyIdempotent

Predict gender from a first name using global data. Returns predicted gender, probability (0–1), and sample size.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesFirst name to predict gender for.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe name that was analyzed
genderYesPredicted gender (male, female, or null if uncertain)
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": {
+    "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: +[
+  {
+    "name": "James"
+  },
+  {
+    "name": "Maria"
+  }
+]
  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 readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable context by specifying the return fields (predicted gender, probability 0–1, sample size) and the global data scope, going beyond the annotations without contradicting them.

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 two sentences that efficiently state the action, scope, and output. It is front-loaded with the verb 'Predict' and contains no unnecessary words or repetition.

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?

For a simple one-parameter tool with full schema coverage, annotations, and output schema, the description is complete. It explains the purpose, output structure, and global data scope, giving an agent everything needed to 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?

The input schema fully describes the only parameter 'name' with the same information ('First name to predict gender for.'). The description does not add additional parameter semantics beyond what the schema provides, so the baseline score of 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 verb and resource: 'Predict gender from a first name using global data.' The mention of 'global data' distinguishes it from the sibling tool predict_gender_country, making the purpose unambiguous.

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 description provides clear context that this is for global data, implying it is not for country-specific predictions. However, it does not explicitly name alternatives or state when not to use it, so it falls just short of a full 5.

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