Create synthetic Australian healthcare test data for Cliniko, including patients and appointments, to validate practice management workflows and integrations.
Generate synthetic test data for Cliniko (Australian healthcare data)
| Name | Required | Description | Default |
|---|---|---|---|
| num_patients | No | Number of patients to create (max 50) | |
| num_appointments | No | Number of appointments to create (max 100) | |
| days_ahead | No | Days ahead to schedule appointments |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only mentions 'synthetic test data' and the context, but does not indicate whether the tool is destructive, requires cleanup, or has side effects like creating real records. Given sibling tools like 'cleanup_test_data', this is a significant gap.
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, front-loaded sentence that efficiently conveys the core purpose. While concise, it could include more details 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?
Given the lack of annotations and output schema, the description should provide more context about what is actually generated (patients, appointments, etc.), whether it is safe for production use, and potential side effects. The current description is incomplete for a bulk data generation tool.
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 input schema has 100% description coverage with clear min/max/default for each parameter. The tool description adds no extra meaning beyond the schema, so the baseline score of 3 is appropriate.
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 clearly states the verb 'generate' and the resource 'synthetic test data' with a specific context (Cliniko, Australian healthcare). It distinguishes itself from sibling tools that create individual entities like patients or appointments.
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 usage for generating test data but provides no explicit guidance on when to use this tool versus the more comprehensive sibling 'generate_comprehensive_test_data' or individual create tools. The context is implied, not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.