naija-faker-mcp

Generate a company

generate_company

Read-only

Generate synthetic Nigerian company records with realistic details for testing or development. Avoid using real business data.

Instructions

Generates a synthetic Nigerian company record.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "generate_company"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`value`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

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

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=false, and destructiveHint=false, covering the safety profile. The description adds the critical 'synthetic' qualifier (indicating fake data) and the geographical scope 'Nigerian', which are not in the annotations. This aligns with the annotations and adds valuable context about the nature of the output.

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 a single sentence that is fully front-loaded with the action and the output type. There is no filler or redundancy; every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simplicity (0 parameters), the existence of an output schema (which documents the return structure), and annotations covering safety, the description is complete for an agent to understand what the tool does and when to invoke it. No additional behavioral or contextual details are needed.

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 tool has zero parameters, so the description has no obligation to explain them. The baseline for 0 params is 4, and since the description doesn't attempt to add parameter details (which would be unnecessary), it meets the baseline appropriately.

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 'Generates a synthetic Nigerian company record' uses a specific verb (generates) and resource (company record), and includes the qualifier 'Nigerian' which distinguishes it from other generators. Among siblings like generate_person, generate_vehicle_record, etc., it clearly identifies the unique output type.

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: this tool generates a synthetic Nigerian company record. While it doesn't explicitly state when not to use it or name alternatives, the purpose is unambiguous and the sibling list makes the domain obvious. There is no misleading information, so it meets the 'clear context, no exclusions' criterion.

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