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Generate fictional US SSN-style identifiers

generate-ssn
Read-onlyIdempotent

Generate synthetic US SSN-style fixtures only, up to 8. Use only to test formatting and field validation in demos. Never use a generated value as anyone's actual Social Security number. Returns synthetic identifiers and does not query Social Security records.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNoOptional deterministic seed for repeatable fake identifiers.
countNoNumber of synthetic SSN-style fixtures, 1–8.
validNoWhether fixtures should pass the generator's structural validity rules, not actual issuance checks.
formattedNoWhether to add standard dashes to the generated identifier.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds real context beyond that: it returns synthetic identifiers and explicitly does not query Social Security records, which is meaningful behavioral disclosure.

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?

Four short sentences, front-loaded with the core action and capped with a safety constraint. The closing "Returns synthetic identifiers" is mildly redundant with "synthetic ... fixtures" earlier, a minor waste.

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 read-only generator with full schema coverage and no output schema, the description adequately signals the return shape and the safety boundary. An agent has everything needed to call it correctly, though return structure details remain implicit.

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%, so seed, count, valid, and formatted are all documented in the schema itself. The description only echoes the count ceiling ("up to 8") and adds no syntax or semantics beyond that, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb (generate) and resource (synthetic US SSN-style fixtures) and the title reinforces it. The resource is unique among siblings like generate-phone-number and generate-card-number, so an agent can tell it apart, though it does not explicitly contrast itself with any sibling.

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?

"Use only to test formatting and field validation in demos" gives explicit when-to-use guidance, and "Never use a generated value as anyone's actual Social Security number" gives a clear when-not. It stops short of naming alternatives such as is-us-tin-safe for validation purposes.

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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