naija-faker-mcp

Generate an education record

generate_education_record

Read-only

Generate a synthetic education record from an age, returning a credible degree and matching course the person could have finished, or null when too young to graduate.

Instructions

Generates a synthetic education record. Pass an age and the degree is one the person lived long enough to earn, with a course that fits the degree's discipline. Returns null when the age is too young to have finished a qualification.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`age`	No	Age of the person the record belongs to
`language`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`value`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv2.1.0Sep 23, 2026

addedInput schema / properties / age

Added value: +{
+  "description": "Age of the person the record belongs to",
+  "maximum": 9007199254740991,
+  "minimum": 0,
+  "type": "integer"
+}

Changed3 schema fields changedv2.0.0Sep 16, 2026

removedInput schema / properties / language / description

Removed value: -"The language of the education record. The available languages are Hausa, Igbo and Yoruba"

addedInput schema / properties / language / enum

Added value: +[
+  "hausa",
+  "igbo",
+  "yoruba"
+]

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "value": {}
+  },
+  "required": [
+    "value"
+  ],
+  "type": "object"
+}

First observedv1.0.1Sep 13, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Beyond the readOnlyHint and destructiveHint annotations, the description discloses meaningful behavior: the degree is chosen to fit the person's age, the course matches the degree's discipline, and null is returned when the age is too young. This gives the agent a precise mental model of the tool's internal logic and 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?

Three sentences, each earning its place: the first defines the operation, the second explains generation logic, the third handles the edge case. The core action is front-loaded and there is no 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?

With an output schema present, return values need no explanation, and the description capably covers purpose, age logic, and null behavior. The only meaningful gap is the undocumented language parameter, which could affect the generated record's content. Overall, the definition is nearly complete for an agent to call 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 description coverage is 50%: the age parameter has a schema description, and the description adds valuable age semantics (determines valid degree). However, the language parameter (hausa/igbo/yoruba) is completely undocumented in both schema and tool description, leaving its effect on the record unknown. The description compensates for age but not for the other parameter.

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 opens with a specific verb and resource: 'Generates a synthetic education record.' It then adds unique behavior (age-based degree feasibility, course matching degree discipline) that distinguishes it from sibling generators like generate_university or generate_work_record. The purpose is unmistakable.

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 for use: 'Pass an age' and explains what the tool does with it, including the null-return condition. It does not explicitly mention alternatives or when-not-to-use, but the usage context is unambiguous enough for an agent to apply it correctly.

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