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Generate fictional people and company records

generate-fake-data
Read-onlyIdempotent

Generate up to 8 synthetic users, companies or addresses. Use to seed demos with made-up users, companies or addresses. For a custom schema and CSV output, use generate-dataset. Creates local synthetic fixtures, not actual identities or verified business records.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYesFixture category: user, company, address or rows.
seedNoOptional deterministic seed to reproduce the same fake records.
countNoRequested number of synthetic records, up to eight.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / count / description
      Added value: +"Requested number of synthetic records, up to eight."
    • addedInput schema / properties / kind / description
      Added value: +"Fixture category: user, company, address or rows."
    • addedInput schema / properties / seed / description
      Added value: +"Optional deterministic seed to reproduce the same fake records."
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new context beyond them: output is local synthetic fixtures, not real identities or verified business records, which reassures the agent nothing external is contacted or mutated.

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?

Three short sentences, front-loaded with the scope constraint and the routing alternative. The second sentence partly restates the first ('made-up users, companies or addresses' repeats the opening), which is minor redundancy but not waste in a small definition.

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?

There is no output schema, yet for a fixture generator the key facts (what is produced, local-only, deterministic via seed) are covered. The only omission is any description of the returned record shape, which is a small gap given the tool's simplicity.

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 coverage is 100%, so kind, seed and count are fully documented in the schema itself; the description only echoes the record cap of eight and the category list. No additional syntax or format meaning is layered on, so the baseline 3 applies.

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?

States a specific verb (generate) plus resource (synthetic users, companies, addresses) and hard scope (up to 8), naming the sibling generate-dataset as the different path. An agent can distinguish this from generate-dataset and the many single-value generators without opening a schema.

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 gives the use case ('seed demos with made-up users, companies or addresses') and the alternative with its selecting condition ('For a custom schema and CSV output, use generate-dataset'). Both when-to-use and when-to-use-something-else are present.

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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