Microapp

dummy-data-generator

Read-onlyIdempotent

Generate tabular test fixtures (JSON or CSV) from a chosen mix of fake fields. Each row is a consistent identity — first/last name match the email; state matches the ZIP prefix. Public-domain data tables; pure JS; deterministic when a seed is passed.

Input Schema

TableJSON Schema

Name	Required	Description
`rows`	Yes	How many rows to generate. Min 1, max 1000.
`seed`	No	Optional integer seed for deterministic output. Same seed + same inputs = same rows. Omit for fresh randomness on each call.
`fields`	Yes	Which columns to include. Pick at least one. Order in the array is the output column order. Allowed: firstName, lastName, email, phone, address, city, state, zip, country, company, date, uuid, loremText, numberRange.
`format`	Yes	Output format. 'json' returns an array of objects; 'csv' returns RFC-4180 CSV with a header row.
`numberMax`	No	Upper bound (inclusive) for the numberRange field. Default 1000.
`numberMin`	No	Lower bound (inclusive) for the numberRange field. Default 0.
`loremWords`	No	How many words in each loremText field. Default 8.

Output Schema

TableJSON Schema

Name	Required	Description
`data`	Yes	Serialized rows in the requested format (JSON string or CSV string).
`rows`	Yes	Number of rows generated.
`fields`	Yes	Column names included, in output order.
`format`	Yes	Format that was used to serialize.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

AddedMay 21, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare this as read-only, idempotent, and non-destructive. The description adds meaningful behavioral context by explaining the cross-field consistency rules and the deterministic-seed property, which are not captured in the annotations or schema.

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?

The description is two concise sentences that front-load the core purpose and then add valuable behavioral detail. No filler or redundant restatement of the tool name.

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?

With a 100% schema coverage and an output schema present, the description provides the right level of high-level context. It explains the tool's unique value (tabulated fake data with coherent identities) while leaving parameter details to the schema.

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?

Because the schema describes all seven parameters with full coverage, the baseline is 3. The description earns extra credit by clarifying how the field parameters interact (e.g., 'first/last name match the email; state matches the ZIP prefix'), giving semantic meaning beyond the individual parameter definitions.

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 opens with 'Generate tabular test fixtures (JSON or CSV) from a chosen mix of fake fields,' giving a specific verb, resource, and output formats. It further distinguishes itself from sibling generators (e.g., random-number, uuid-generator) by emphasizing row-level consistency and public-domain data tables.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly frames when to use the tool: for creating tabular test fixtures with realistic identity correlations (e.g., first/last name match email, state matches ZIP). It does not explicitly name alternatives or exclusions, but the context is concrete enough to guide selection among the sibling list.

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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TDQS

A3.9/5.0

Disambiguation4/5

Most tools have distinct purposes. However, 'hex-to-rgb' is redundant with 'color-converter', which already handles hex-to-RGB conversion, causing potential confusion.

Naming Consistency4/5

Names follow a consistent lowercase-with-hyphens style, but vary in pattern (e.g., 'angle-converter', 'average-calculator', 'dedup-lines'). One tool ('internal-do-not-call') deviates from the descriptive norm.

Tool Count2/5

With 46 tools, the server is heavily populated. Many converters could be merged into a generic unit converter, and there is redundancy, making the surface unnecessarily large for a single server.

Completeness4/5

The server covers a broad range of utility domains: converters, text processing, math, cryptography, etc. Minor redundancies exist (e.g., hex-to-rgb vs color-converter), but the set is otherwise comprehensive.

Resources

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