naija-faker-mcp

Generate geographically consistent people

generate_consistent_people

Read-only

Generate synthetic Nigerian people records with matching identity, address, state, and LGA fields. Specify gender, language, and count for tailored test data.

Instructions

Generates an array of synthetic people with geographically coherent identity, address, state, and LGA fields. Accepts an optional positive count and returns 10 records by default.

Input Schema

TableJSON Schema

Name	Required	Description
`count`	No	The number of people to generate
`gender`	No	The person's gender
`language`	No	The person's language

Output Schema

TableJSON Schema

Name	Required	Description	Default
`items`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed8 schema fields changedv2.0.0Sep 16, 2026

addedInput schema / properties / count / maximum
```
Added value: +9007199254740991
```
addedInput schema / properties / count / minimum
```
Added value: +1
```

changedInput schema / properties / count / type

Previous value: -"number"New value: +"integer"

changedInput schema / properties / gender / description

Previous value: -"The gender of the person data. The accepted gender values are male and female"New value: +"The person's gender"

addedInput schema / properties / gender / enum
```
Added value: +[
+  "male",
+  "female"
+]
```

changedInput schema / properties / language / description

Previous value: -"The language of the person data. The available languages are Hausa, Igbo and Yoruba"New value: +"The person's language"

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": {
+    "items": {
+      "items": {
+        "additionalProperties": false,
+        "properties": {
+          "address": {
+            "type": "string"
+          },
+          "email": {
+            "type": "string"
+          },
+          "firstName": {
+            "type": "string"
+          },
+          "fullName": {
+            "type": "string"
+          },
+          "lastName": {
+            "type": "string"
+          },
+          "lga": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "phone": {
+            "type": "string"
+          },
+          "state": {
+            "type": "string"
+          },
+          "title": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "title",
+          "firstName",
+          "lastName",
+          "fullName",
+          "email",
+          "phone",
+          "address",
+          "state",
+          "lga"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "items"
+  ],
+  "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?

The annotations already declare readOnlyHint=true and destructiveHint=false, so the description correctly adds behavioral context beyond that by noting the array nature, the optional positive count, and the default of 10 records. This is useful for an agent deciding whether to pass count or rely on the default.

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 tight sentences. The primary behavior and scope are front-loaded, the default-count behavior is included, and there is no redundant repetition of schema or annotation details.

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?

Given the output schema exists and annotations cover safety, the description is mostly complete for correctly invoking the tool. The only notable gap is that 'geographically coherent' is not elaborated enough to tell an agent exactly how fields are constrained across states, LGAs, and identity, but this does not block basic usage.

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?

The input schema has 100% coverage of all three parameters, so the baseline is 3. The description adds value by stating that count is optional and that omitting it yields 10 records by default, which is not present in the schema. It does not discuss gender or language, but the schema already describes those fully.

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 states a specific verb and resource: 'Generates an array of synthetic people' with 'geographically coherent identity, address, state, and LGA fields.' This clearly distinguishes it from other people generators, especially the singular generate_consistent_person, by explicitly saying it returns an array.

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 phrase 'geographically consistent' implies the intended use, but the description does not explicitly state when to choose this tool over generate_people, generate_consistent_person, or generate_detailed_people. There are no exclusions or alternative routing, so usage guidance is only implied.

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