Convert SQL CREATE TABLE statements or one sample JSON object into a MockHero schema that can be passed to generate_test_data.
| Name | Required | Description | Default |
|---|---|---|---|
| sql | No | SQL CREATE TABLE statement or statements. | |
| sample_json | No | Single example JSON object to infer fields from. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds the conversion behavior itself but does not disclose additional traits like error handling, limitations, or output details. With annotations covering safety, a 3 is appropriate.
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 conveys all essential information without redundancy. Every phrase adds value.
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 tool's moderate complexity, the read-only annotations, and the presence of an output schema, the description is sufficiently complete. It covers the two input modes and the purpose. It could mention the anyOf constraint explicitly, but the schema already handles that, so a slight deduction is reasonable.
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 coverage is 100%: both parameters (sql and sample_json) are described in the input schema. The description mentions them but adds no extra detail beyond what the schema already provides, so the baseline of 3 is justified.
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 ('Convert') and clearly identifies the resources (SQL CREATE TABLE statements, sample JSON object) and the output (MockHero schema). It distinguishes itself from sibling tools like generate_test_data by noting the schema is 'passed to generate_test_data'.
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 when to use this tool: when you have SQL or a sample JSON object and need a schema for generate_test_data. It provides clear context but does not explicitly mention alternatives or exclusions, which would make it a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clear, distinct purpose. generate_test_data and generate_from_template could overlap, but descriptions distinguish custom/plain-English generation from pre-built templates. The payment lifecycle tools (create_agent_checkout, check_agent_checkout_status, claim_agent_api_key) are sequential and clearly scoped.
All tool names follow a consistent snake_case verb_noun pattern (e.g., create_agent_checkout, list_field_types, generate_test_data). generate_from_template uses verb_preposition_noun but remains in the same style. No mixing of conventions.
9 tools is well-scoped for a test data generation service with monetization. Each tool earns its place, covering data generation, schema discovery, and payment flow without redundancy or bloat.
The tool set covers the core generation workflows, schema construction, and the full checkout-to-key-claim lifecycle. Minor gaps exist, such as no API key management (e.g., revoke or refresh) and no template customization, but these are not critical for the primary use case.
