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generate_from_template

Generate realistic test data from a pre-built MockHero template: ecommerce, blog, saas, or social. Small template previews up to 100 records can run free; larger or production usage requires a MockHero API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
scaleNo
formatNojson
localeNo
api_keyNoOptional MockHero API key for no-auth MCP clients. Prefer the Authorization header when the client supports it.
templateYes
sql_dialectNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

B3.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds behavioral context beyond annotations by disclosing the free-tier limit and API key requirement, which are not indicated by readOnlyHint, openWorldHint, idempotentHint, or destructiveHint. However, it does not explain side effects, resource consumption, error conditions, or whether the operation is deterministic despite idempotentHint=false. It adds some value but lacks comprehensive behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with two sentences front-loading the core purpose and then adding essential usage context. It contains no fluff and every sentence earns its place, though it could potentially be more structured by explicitly linking the limit to a parameter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 7 parameters and an output schema, the description covers the essential template selection and free-tier limit but leaves out important contextual details such as how to control record count (scale), output format, localization, and SQL dialect. The output schema may cover return values, but the description alone is not fully complete for effective invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With only 14% schema description coverage, the description was expected to compensate for undocumented parameters. It does mention template types (duplicating the schema enum) and the 100-record limit, but it does not explain the meaning or usage of seed, scale, locale, format, sql_dialect, or their relationships. The description adds minimal semantic value beyond the schema, failing to compensate for the low coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: generating realistic test data from pre-built MockHero templates, listing specific template types (ecommerce, blog, saas, social). It distinguishes itself from sibling tools by focusing on pre-built templates, but does not explicitly differentiate from generate_test_data, so it lacks full sibling distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides usage conditions: free previews up to 100 records and API key requirement for larger/production use, which is useful context. However, it does not explicitly state when to prefer this tool over alternatives like generate_test_data, nor does it mention exclusions or alternative tools, leaving usage guidance implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness4/5

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.