generate_license_plateGenerate synthetic Nigerian license plate data, optionally by state, to provide realistic test data without using real vehicle registration information.
Generates a synthetic Nigerian license plate.
| Name | Required | Description | Default |
|---|---|---|---|
| state | No |
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes |
Changes observed during successful MCP inspections.
v2.0.0Input schema / properties / state / descriptionRemoved value: -"The state of the preferred license plate"Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "http://json-schema.org/draft-07/schema#",
+ "additionalProperties": false,
+ "properties": {
+ "value": {}
+ },
+ "required": [
+ "value"
+ ],
+ "type": "object"
+}v1.0.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnly and non-destructive behavior. The description adds the useful context that the output is synthetic and Nigerian-specific, but it does not disclose plate format, randomness, or any generation constraints beyond that.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single compact sentence that is front-loaded and contains no filler. Every word contributes to the meaning without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output schema and annotations cover some context, but the description leaves the state parameter entirely unexplained, including whether it is a Nigerian state name, abbreviation, or something else. This gap prevents an agent from confidently invoking the tool with a correct state value.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema includes a single 'state' parameter with 0% description coverage, and the tool description does not mention state at all. There is no guidance on allowed values, default behavior, or how state affects the generated plate, so the description adds no parameter meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Generates'), a concrete resource ('Nigerian license plate'), and the synthetic nature of the data. It is clear, though it does not explicitly differentiate itself from related sibling generators such as generate_vehicle_record.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance about when to choose this tool over alternatives like generate_vehicle_record or other data generators. Intended usage is only implied by the tool name and resource, not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.