ScorpTech x402 Tools

Fake Data

fake_data

Read-onlyIdempotent

Generate realistic, reproducible fake test data from a field schema: up to 1,000 records with 44 field types (names, usernames, emails consistent with the name, phones, companies, job titles, US addresses, countries, coordinates, dates, numbers, prices, UUIDs, IPs, URLs, lorem text, enums with weights, sequences, templates referencing other fields, test card numbers), per-field null rate and uniqueness, as JSON, CSV or NDJSON. Seeded (the seed is returned) so the same request gives the same data. Safe by design: only example.* domains, 555-01xx phone numbers, documentation IP ranges and public card test numbers. Price: $0.003 USDC per successful call (x402, Solana). Same tool over HTTPS: POST https://scorptech.it.com/api/x402/v1/fake-data

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "fields": "<fields>"
    },
    "name": "fake_data"
  }
}

Input Schema

Name	Required	Description	Default
`seed`	No	Reproducible output (a random seed is chosen and returned when omitted)
`count`	No
`fields`	Yes	Field name => type name, or {type, ...options} (min / max / decimals / from / to / values / weights / start / step / template / value / nullRate / unique)
`format`	No		json

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 23, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Adds substantial context beyond the annotations: deterministic seeding with the seed returned, explicit safety guarantees (example.* domains, 555-01xx phones, documentation IP ranges, public test card numbers), the 1,000-record cap, and a per-call price of $0.003 USDC via x402 on Solana. The payment requirement in particular is invocation-critical and cannot be inferred from readOnlyHint/idempotentHint.

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?

Front-loaded with purpose, then constraints, safety, and pricing in tight sentences. The long comma-separated field-type catalog is dense but earns its place as selection evidence; a reader can skim the first clause and stop if it's not the tool they need.

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?

No output schema exists, yet the description states the return formats, the seed round-trip, and the cost/payment model — everything an agent needs to call it correctly and anticipate the outcome. Nothing material is missing for a four-parameter generator.

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?

Half the parameters lack schema descriptions (count, format), and the description compensates by stating the 1,000-record cap and listing the three output formats, plus a rich catalog of field types and per-field options (null rate, uniqueness, weights, templates referencing other fields). The nested `fields` object semantics are well covered; only the seed's interaction with the returned value is left partly implicit.

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?

Opens with a specific verb+resource: 'Generate realistic, reproducible fake test data from a field schema', then bounds it (up to 1,000 records, 44 field types, JSON/CSV/NDJSON). No sibling in the list does schema-driven data generation, so the tool is unmistakable.

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?

Establishes the use case (test data) and the reproducibility contract (seed returned, same request = same data), plus the alternative transport (POST to the HTTPS x402 endpoint). It stops short of naming when NOT to use it or which sibling (e.g. random_draw, id_generate) to prefer for a single value.

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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