generate_universityGenerate a synthetic Nigerian university record to supply realistic academic data for testing and development use cases.
Generates a synthetic Nigerian university record.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes |
Changes observed during successful MCP inspections.
v2.0.0Output 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, so the safety profile is covered. The description adds the important context that the record is synthetic and Nigerian-specified, which goes beyond annotations, but it does not disclose details like output structure or possible variability. This is adequate but not rich.
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?
A single sentence that immediately states the tool's purpose with the key qualifying details ('synthetic', 'Nigerian', 'university record'). There is no wasted wording and the structure efficiently front-loads the action and object.
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?
For a parameterless generation tool with an output schema available, this description is fully sufficient. An agent knows what to expect (a synthetic Nigerian university record) and can rely on the output schema for return structure. No additional context is needed.
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?
There are zero parameters, so the schema provides full 100% coverage and there is nothing for the description to clarify. The baseline for no parameters is 4, and the description appropriately focuses on the output rather than any parameters.
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 uses a specific verb ('Generates') and a specific resource ('synthetic Nigerian university record'), making the tool's function unmistakable. It is distinct from siblings like generate_person or generate_education_record by targeting a university entity, even though it does not explicitly name the differentiating sibling.
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 implies the tool should be used when a synthetic Nigerian university record is needed, which is clear from the purpose. However, it provides no explicit guidance about when not to use it or how it differs from overlapping siblings such as generate_education_record or generate_company.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.