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Predict Age Country

predict_age_country
Read-onlyIdempotent

Estimate someone's age from their first name within a specific country (e.g., 'US', 'FR', 'JP'). Returns predicted age and regional confidence count.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesFirst name to predict age 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
countryYesISO country code for the region used in prediction
sample_sizeYesNumber of data points used for regional prediction (confidence measure)
predicted_ageYesPredicted age based on country-specific statistics, or null if unavailable

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "country": {
+      "description": "ISO country code for the region used in prediction",
+      "type": "string"
+    },
+    "name": {
+      "description": "The name that was analyzed",
+      "type": "string"
+    },
+    "predicted_age": {
+      "description": "Predicted age based on country-specific statistics, or null if unavailable",
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "sample_size": {
+      "description": "Number of data points used for regional prediction (confidence measure)",
+      "type": "number"
+    }
+  },
+  "required": [
+    "name",
+    "predicted_age",
+    "sample_size",
+    "country"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "country_code": "US",
+    "name": "John"
+  },
+  {
+    "country_code": "DE",
+    "name": "Hans"
+  }
+]
  3. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds meaningful context by specifying the return behavior (predicted age and regional confidence count) and the country-scoped nature of the prediction. It does not contradict annotations and provides sufficient transparency for a simple read operation.

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 concise sentences. The first sentence front-loads the core function, and the second states the return values. There is no filler or redundant information.

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 tool with annotations and an output schema, the description is largely complete: it explains what the tool does and what it returns. The only gap is the absence of explicit usage guidance relative to sibling tools, but this is a minor omission given the clarity of the function and the presence of structured metadata.

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 provides 100% coverage of both parameters with clear descriptions. The tool description adds no new semantic detail beyond the schema; the country examples ('US', 'FR', 'JP') are repeated from the schema. The phrase 'regional confidence count' hints at the country_code's role, but this is already implied by the schema's 'to localize the prediction.'

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 uses the specific verb 'estimate' with the resource 'age from first name' and the scope 'within a specific country', clearly distinguishing it from the sibling tool predict_age. It also provides examples of country codes and states the return values (predicted age and regional confidence count), 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 Guidelines3/5

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

The description implies usage when a specific country is known ("within a specific country"), but it does not explicitly state when to use this tool versus alternatives like predict_age. No exclusions or alternative recommendations are mentioned, so guidance is implicit rather than explicit.

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

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