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Generate a small synthetic dataset

generate-dataset
Read-onlyIdempotent

Generate synthetic rows as JSON, JSONL or CSV. Use for data import/export tests; use generate-fake-data for preset people or company fixtures. Returns at most eight records without accessing a database.

Input Schema

NameRequiredDescriptionDefault
seedNoOptional deterministic seed for reproducible test rows.
countNoNumber of records to generate, 1–8, default 3.
formatNoOutput format: json, jsonl or csv.
schemaNoOptional map of field names to upstream synthetic field specifications; no real records.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds real behavioral context beyond that: the hard cap of eight records and the fact that no database is accessed. It doesn't discuss seed reproducibility or error behavior, but the added disclosure is meaningful.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler; the output-format clause is front-loaded, followed by the routing guidance and the record cap. Every clause earns its place.

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?

For a 4-parameter, zero-required tool with a nested schema object and no output schema, the description covers output shape (at most eight records), formats, and sibling routing. The nested field-specification object is left to the schema, which is reasonable, though a note on how seed affects output would complete it.

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 description coverage is 100% with 4 documented parameters (seed, count, format, schema), including the enum and the 1-8 bound, so the schema carries the parameter burden. The description only echoes the format list and the count ceiling, adding no syntax or semantics beyond what is already structured.

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 and resource (generate synthetic rows) plus the concrete output formats (JSON, JSONL, CSV), so an agent knows exactly what the tool produces. It explicitly distinguishes itself from the sibling generate-fake-data, which shares the 'generate' prefix in this crowded family.

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?

Gives a positive use case (data import/export tests) and names the alternative tool with the condition that selects it (generate-fake-data for preset people or company fixtures). This is explicit when-to-use plus when-to-use-something-else, which is exactly what the routing decision requires.

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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