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generate_fake_data

Generate realistic fake data instantly — stateless, nothing is created or stored, no project or adminKey needed. Ready-made resource shapes (FakerAPI-compatible): persons, users, addresses, companies, books, products, texts, images, places, credit_cards (credit cards are Luhn-valid; book EAN13/ISBN13 checksums are real; image URLs are live SVG placeholders served by Mockbird). Or pass fields for a custom shape: an object mapping output key → type, with type ∈ counter|uuid|number|boolean|word|text|longText|firstName|lastName|name|email|phone|date|dateTime|image|streetAddress|streetName|buildingNumber|city|postcode|state|country|countryCode|latitude|longitude|vat|website|company_name|card_type|card_number|card_expiration|ean|upc|pokemon|null. seed makes output deterministic — same seed + shape returns identical rows forever (reproducible fixtures). Need the data HOSTED instead? create_project / add_resource serve seeded collections at a live REST URL with full CRUD, filters, and persistence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNoDeterministic seed — same seed returns the same rows. Optional.
fieldsNoCustom shape: {outputKey: type}, e.g. {"id":"counter","name":"firstName","mail":"email","signup":"dateTime"}. Optional.
paramsNoExtra FakerAPI-compatible query params, e.g. {"_gender":"female"}, {"_price_min":10,"_price_max":500}, {"_characters":500}, {"_width":640}. Optional.
quantityNoRows to generate (1-100, default 5).
resourceNoOne of persons|users|addresses|companies|books|products|texts|images|places|credit_cards. Default persons. Ignored when fields is set.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It explicitly states that nothing is created or stored, no project or adminKey is needed, and that seeding produces deterministic, reproducible output. It does not mention rate limits or error behavior, but the key side-effect-free nature is transparent.

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 a bit long, but each sentence adds meaningful context: statelessness, resource shapes, custom fields, determinism, and the hosted alternative. It could be tightened by removing the repeated type list in prose, but it is not padded with fluff.

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?

The description gives enough context for an agent to decide when and how to call the tool, including parameter behavior and the relationship to sibling tools. It does not describe the response format, but since there is no output schema and fake-data output is generally self-evident, this is not a critical gap.

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

Parameters4/5

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

The input schema already covers all five parameters with descriptions, so the baseline is high. The tool description adds valuable semantics beyond the schema, such as the default resource ('Default persons'), the precedence rule ('Ignored when fields is set'), and concrete examples for fields and params.

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 that the tool generates realistic fake data instantly and is stateless, creating or storing nothing. It also names the specific resource types and distinguishes itself from hosted alternatives like create_project and add_resource.

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?

It explicitly explains when to use this tool versus hosted alternatives: 'Need the data HOSTED instead? create_project / add_resource...' This gives clear selection criteria and directs the agent away from the wrong sibling tools.

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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