naija-faker-mcp

Generate a work record

generate_work_record

Read-only

Generate a synthetic work record from age and graduation year, producing job seniority, position, and salary band based on experience.

Instructions

Generates a synthetic work record. Pass an age and graduation year and the job starts after the degree, with seniority, position, and salary band following years of experience.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`age`	No	Age of the person the record belongs to
`graduationYear`	No	Year the person graduated; employment starts after it

Output Schema

TableJSON Schema

Name	Required	Description	Default
`value`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedv2.1.0Sep 23, 2026

addedInput schema / $schema

Added value: +"http://json-schema.org/draft-07/schema#"

addedInput schema / properties / age

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

addedInput schema / properties / graduationYear

Added value: +{
+  "description": "Year the person graduated; employment starts after it",
+  "maximum": 9007199254740991,
+  "minimum": -9007199254740991,
+  "type": "integer"
+}

Changed1 schema field changedv2.0.0Sep 16, 2026

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

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds value by explaining the generation logic: the job starts after graduation and seniority/position/salary scale with years of experience. This gives the agent insight into how the output is derived, which is 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?

Two concise sentences with no redundancy. The core purpose is front-loaded, and the logic explanation is tight. Every sentence earns its place.

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?

The tool is simple with two well-documented parameters and an output schema that presumably describes the return structure. The description covers the essential generation logic. It does not mention optionality or edge cases, but given the schema and output schema richness, nothing critical is missing for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (both parameters have descriptions). The description reinforces the relationship between age and graduationYear ('job starts after the degree') and introduces that seniority/position/salary depend on experience, which is not fully captured in the schema descriptions. This adds meaningful context for correctly interpreting the parameters.

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?

States a specific verb ('Generates') and resource ('synthetic work record'), and the description explains the relationship between the inputs (age, graduation year) and the output (job starts after degree, seniority/position/salary follow experience). This clearly distinguishes it from sibling tools like generate_salary or generate_title, which target individual fields.

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 the use case (generating a complete work record) but does not explicitly contrast it with alternatives like generate_salary or generate_title, nor does it state when not to use this tool. It provides the input logic but no explicit routing guidance beyond the obvious composite nature of the tool.

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