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data_mock_api_generator

Read-only

Generate mock REST API endpoints by defining paths, methods, field types, and record counts. Returns fabricated records grouped by route for multi-endpoint API fixtures.

Instructions

Mock API Generator (Build Mock REST API From JSON Schema). Generate a small in-memory mock REST API document from a JSON schema template: given an array of endpoints (each a path, HTTP method, record count, and a list of typed fields), it fabricates fake records per endpoint and returns them grouped by path. Field types include uuid, integer, float, boolean, full_name, first_name, last_name, email, phone, iso_date, iso_datetime, sentence, paragraph, and enum. Use this when you need multi-endpoint API fixtures keyed by route; use data_faker for a single flat list of richly namespaced person/finance fields, data_random_data_generator for one flat record set across many primitive types with CSV/TSV/NDJSON output, or data_sample_data_generator for curated ready-made domain datasets (users, orders, logs). Output is random and non-idempotent — an optional seed makes record bodies reproducible, but the generatedAt timestamp still varies each call. Runs locally, read-only, contacts no external service, and is rate-li

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
templateYesSchema describing the endpoints to fabricate.
seedNoOptional seed; when set, record bodies are reproducible (generatedAt still varies). Omit for crypto-random output.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successNoTrue when generation succeeded.
infoNoSummary of the generated document.
endpointsNoOne entry per requested endpoint.
Behavior5/5

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

Description adds detail beyond annotations: randomness, seed reproducibility (with caveat about generatedAt), local execution, read-only nature, and rate limiting. Consistent with annotations (readOnlyHint=true, idempotentHint=false) and enriches agent understanding.

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?

Front-loaded with purpose, well-organized into purpose, usage comparison, and behavioral notes. However, it is somewhat verbose and includes redundant information (e.g., field types already in schema). Minor truncation at end does not impair clarity.

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

Completeness4/5

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

Covers purpose, usage, input structure, field types, behavioral traits, and alternatives. Output format is mentioned as 'grouped by path' and output schema provides complete structure. Truncation of final sentence ('rate-li') slightly reduces completeness but core information is present.

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

Parameters3/5

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

Schema has 100% description coverage, so the description adds little new parameter information. It summarizes field types and endpoint structure, but these are already detailed in the schema. Baseline 3 is appropriate.

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

Purpose5/5

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

The description clearly states the tool generates mock REST APIs from JSON schema with multiple endpoints. It explicitly distinguishes itself from sibling tools (data_faker, data_random_data_generator, data_sample_data_generator) by naming them and describing their different use cases.

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

Usage Guidelines5/5

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

Provides explicit guidance: 'Use this when you need multi-endpoint API fixtures keyed by route' and lists three specific alternatives with concise descriptions of when to use each.

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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