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Jambozx

OnlineCyberTools MCP (280+ filterable tools)

data_sample_data_generator

Read-only

Create demo datasets for nine predefined business shapes including users, orders, and products. Output as JSON, NDJSON, CSV, or TSV with optional seed for reproducibility.

Instructions

Sample Data Generator. Generate ready-made demo datasets for nine fixed business shapes (users, orders, products, log_lines, transactions, inventory, tickets, employees, analytics_events), each with a curated column set, emitted as JSON, NDJSON, CSV (RFC 4180), or TSV. Pick this when you want a recognisable, opinionated table for one of those shapes; use data_faker to compose arbitrary field-by-field records, data_random_data_generator for schema-driven random rows, or data_mock_api_generator to stand up mock endpoints. Runs locally: read-only, non-destructive, contacts no external service, and rate-limited (60 requests/minute for anonymous callers). Output is NON-deterministic by default (cryptographic randomness); pass a string seed for reproducible rows. Returns the formatted output string plus the parsed records array.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
shapeYesDataset preset to generate; each shape has a fixed column set.
countNoNumber of records to generate (1 to 500).
seedNoOptional seed string for reproducible output (max 1024 chars); omit or null for cryptographic randomness.
formatNoOutput serialisation: json (pretty array), ndjson (one object per line), csv (RFC 4180), or tsv.json

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successNoWhether generation succeeded.
shapeNoThe shape preset that was generated.
countNoNumber of records returned.
formatNoThe output format applied (json, ndjson, csv, or tsv).
outputNoThe serialised dataset in the requested format.
recordsNoThe generated records as objects, before serialisation.
Behavior5/5

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

Beyond annotations (readOnlyHint true), description adds details: runs locally, non-destructive, rate-limited, non-deterministic by default but supports seed for reproducibility. No contradiction.

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 a single, well-structured paragraph that front-loads purpose and provides necessary details without redundancy. Slightly lengthy but efficient.

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 all aspects: shapes, count, seed, format, return value, determinism, rate limits, and local execution. Output schema exists; description complements it.

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%; description adds meaning by explaining shape examples, count range, seed purpose, format types, and return value structure, going beyond the schema.

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?

Description clearly states 'generate ready-made demo datasets' for nine fixed shapes, and distinguishes from siblings like data_faker and data_random_data_generator by naming them explicitly.

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 advises when to use this tool ('recognisable, opinionated table') and when to use alternatives ('use data_faker...'), providing clear context.

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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