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Jambozx

OnlineCyberTools MCP (280+ filterable tools)

data_data_faker

Read-only

Generate fake data for a single field type such as person.fullName or internet.email using Faker.js presets. Supports an optional seed for reproducible outputs.

Instructions

Data Faker (Faker.js-style field presets). Generate fake values for one Faker.js-style field preset (44 namespaced fields across person, internet, address, phone, company, commerce, date, lorem, finance, and system) such as person.fullName, internet.email, address.zip, phone.imei, finance.creditCardNumber. Output is non-deterministic by default (CSPRNG); pass an optional string seed for reproducible runs via a non-cryptographic xoshiro128** generator. Use this when you need many values of a SINGLE field type; use data_random_data_generator or data_sample_data_generator instead to build multi-column records or curated datasets (users, orders) as JSON/CSV/TSV, or data_mock_api_generator to stand up mock endpoints. Runs locally: read-only, non-destructive, contacts no external service, and is rate-limited (60 requests per minute anonymous, 120 authenticated). Returns the resolved preset, the count, and an array of generated string values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
presetYesField preset to generate, namespaced as group.field (e.g. person.fullName, internet.email, finance.iban). Must be one of the 44 enum values.
countNoHow many values to generate. Integer 1 to 1000; defaults to 1.
seedNoOptional string seed (max 1024 chars) for reproducible output via a non-cryptographic xoshiro128** generator. Omit or null for cryptographically random values. Never use seeded output for tokens, salts, keys, IVs, or nonces.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successNoTrue when generation succeeded; false on a validation error.
presetNoThe resolved preset that was generated.
countNoNumber of values returned (matches the requested count on success).
valuesNoThe generated fake values, one string per requested count.
errorNoPresent only on failure; the validation error message.
Behavior5/5

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

Beyond annotations (readOnlyHint=true, destructiveHint=false), the description adds: runs locally, non-destructive, contacts no external service, rate-limited, non-deterministic by default with optional seed for reproducibility. No contradictions.

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?

Description is detailed but front-loaded with key purpose. Could be slightly more concise but the level of detail is justified given the tool's complexity and sibling differentiation.

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, behavioral aspects, parameter semantics, and output structure (returns preset, count, array). With output schema present, no further explanation needed.

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

Parameters5/5

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

Schema coverage is 100%; description reinforces parameter meanings: preset namespaced, count range, seed optional with security warning. Adds context not in schema (e.g., seed max length, not for security use).

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 it generates fake values for one Faker.js-style field preset, listing namespaces and examples. It distinguishes from sibling tools like data_random_data_generator and data_sample_data_generator, making the purpose specific and unambiguous.

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 states when to use this tool (need many values of a single field type) and when to use alternatives (data_random_data_generator for multi-column records, data_mock_api_generator for mock endpoints). Also mentions rate limits.

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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