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Jambozx

OnlineCyberTools MCP (280+ filterable tools)

data_random_data_generator

Read-only

Generate custom fake records from a user-defined field schema, returning random values for 22 field types in JSON, NDJSON, CSV, or TSV. Supports optional seed for reproducible output.

Instructions

Generate Custom Random Fake Records. Generate fake records from a user-defined field schema, returning random values for 22 field types (first_name, last_name, full_name, email, phone, company, street_address, city, state, zip, country, country_code, iso_date, iso_datetime, uuid, integer, float, boolean, word, sentence, paragraph, enum) as JSON, NDJSON, CSV (RFC 4180), or TSV. Output is non-deterministic (CSPRNG via crypto.getRandomValues) unless a seed string is supplied, in which case generation is fully reproducible via deterministic xoshiro128** (never use seeded output for tokens, salts, keys, IVs, or nonces). Use this when you control the exact record schema (field names and per-field types/options). Use data_data_faker instead for realistic Faker.js-style preset fields chosen by name without per-field options; use data_sample_data_generator for ready-made curated demo datasets (users, orders, products, logs); use math_random_number_generator when you only need standalone random numbers, not records. Re

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsYesField schema: 1 to 50 field specifications. Each row of output contains one value per field. Field names must be unique.
countYesNumber of records to generate. Required, 1 to 1000.
seedNoOptional seed (max 1024 chars). Omit or null for non-deterministic CSPRNG output; supply to get deterministic reproducible output. Do not use seeded output for security tokens.
formatNoOutput serialization for the output field. Default json (pretty-printed). csv is RFC 4180.json

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successNoTrue on success; false with an error string on bad input.
countNoNumber of records generated (echoes the request count).
fieldsNoNormalised field specifications used, each with name, type, and resolved options.
formatNoOutput format used (json, ndjson, csv, or tsv).
outputNoRecords serialized as a single string in the chosen format.
recordsNoParsed records; each item is an object keyed by field name.
Behavior5/5

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

Discloses non-deterministic CSPRNG vs deterministic seeded output with xoshiro128**, and warns against using seeded output for security purposes. Annotations already indicate readOnlyHint=true, and description adds valuable behavioral detail.

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?

Two sentences that front-load purpose and then cover randomness and guidelines. Brief but effective, though appears truncated at the end ('Re').

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

Completeness5/5

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

Covers purpose, usage, randomness, security, and output formats. With full schema descriptions and an output schema, the description is complete for the tool's complexity.

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?

Schema coverage is 100%, so baseline is 3. The description adds context about output formats, seed behavior, and field types beyond what schema provides, justifying a 4.

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 custom random fake records with user-defined schema. It lists 22 field types and distinguishes from sibling tools like data_data_faker, data_sample_data_generator, and math_random_number_generator.

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?

Explicitly tells when to use this tool: 'Use this when you control the exact record schema' and provides alternatives for other cases, naming specific 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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