generate_education_recordGenerate synthetic education records in Yoruba, Igbo, or Hausa to provide realistic Nigerian academic data for development and testing.
Generates a synthetic education record.
| Name | Required | Description | Default |
|---|---|---|---|
| language | No |
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes |
Changes observed during successful MCP inspections.
v2.0.0Input schema / properties / language / descriptionRemoved value: -"The language of the education record. The available languages are Hausa, Igbo and Yoruba"Input schema / properties / language / enumAdded value: +[
+ "hausa",
+ "igbo",
+ "yoruba"
+]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 declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds 'synthetic,' indicating the data is fabricated rather than real, which is useful. It doesn't disclose behavior like randomness or standalone output, but annotations lower the burden.
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 one short, direct sentence with no wasted words. It states the action and the synthetic nature of the output efficiently, though it could have included more useful context without becoming verbose.
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 tool is simple, has an output schema, and annotations cover side effects. However, the description gives no guidance on what an education record includes or when to select this among many sibling generators, leaving some ambiguity.
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?
Schema description coverage is 0%, and the description does not mention the optional 'language' parameter at all. The schema's enum is clear on its own, but the description was supposed to compensate for low coverage and does not.
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 clear verb and resource: it generates a synthetic education record. The word 'synthetic' usefully signals fabricated data. It doesn't explicitly distinguish from sibling generators like generate_work_record, but the resource is specific enough.
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?
There is no guidance about when to use this tool versus sibling generators such as generate_work_record, generate_university, or generate_detailed_person. The context signal listing many similar sibling tools makes this omission noticeable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.