agify

Predict Age

predict_age

Read-onlyIdempotent

Estimate someone's age from their first name using global statistics. Returns predicted age and confidence count based on name frequency data.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes	First name to predict age for.

Output Schema

TableJSON Schema

Name	Required	Description
`name`	Yes	The name that was analyzed
`sample_size`	Yes	Number of data points used for prediction (confidence measure)
`predicted_age`	Yes	Predicted age based on name statistics, or null if unavailable

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedMay 8, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "name": {
+      "description": "The name that was analyzed",
+      "type": "string"
+    },
+    "predicted_age": {
+      "description": "Predicted age based on name statistics, or null if unavailable",
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "sample_size": {
+      "description": "Number of data points used for prediction (confidence measure)",
+      "type": "number"
+    }
+  },
+  "required": [
+    "name",
+    "predicted_age",
+    "sample_size"
+  ],
+  "type": "object"
+}

Changed1 schema field changedMay 6, 2026

addedInput schema / examples

Added value: +[
+  {
+    "name": "John"
+  },
+  {
+    "name": "Maria"
+  }
+]

First observedApr 14, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is covered. The description adds value by disclosing the return behavior ('Returns predicted age and confidence count based on name frequency data'), which goes beyond the 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 two sentences and 21 words, immediately stating the core purpose and return value. Every word contributes meaning, with no redundancy or filler.

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 one-parameter tool with an output schema and strong annotations, the description adequately covers the prediction logic and what is returned. It does not discuss edge cases like uncommon names or data sources, but given the low complexity and existing schema, this is not a critical gap.

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 schema already describes the 'name' parameter as 'First name to predict age for' with examples, so schema coverage is 100%. The description merely repeats 'first name' without adding new syntactic or semantic details, thus offering no additional value beyond the schema.

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 a specific verb 'Estimate' and clearly identifies the resource ('someone's age from their first name') and method ('using global statistics'). It distinguishes itself from the sibling tool predict_age_country by specifying global scope, so the agent knows the difference.

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 'using global statistics' implies this tool is the global variant, contrasting with predict_age_country among siblings. However, it does not explicitly state when not to use it or provide direct alternative references, so it misses the full 'when/when-not' guidance.

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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