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generate_uuid

UUID Generator — Generate unique identifiers — UUID v4 or v7, ULID, Nano ID, or plain random hex. Exists so a workflow gets real identifiers instead of asking a language model to invent them. [category: generate]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoOut-of-range values are pulled back to the nearest allowed value rather than refused, and the reply says so when it happened.
formatNoA name we do not recognise is refused and the five accepted names are listed. It is not quietly turned into a v4 — a caller that asked for a ULID and got a UUID has data of the wrong shape that looks perfectly healthy until something downstream breaks.v4
lengthNoHow many characters long each Nano ID is. Only used when the kind above is Nano ID.
hyphensNoTurn this off for the 32-character form with no dashes. Only applies to the two UUID kinds.
uppercaseNoReturn the letters in capitals. Nano ID and ULID have their own fixed alphabets and ignore this.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior3/5

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

Annotations already signal no destructive behavior, and the description adds little behavioral detail beyond listing formats and the rationale. The schema descriptions elsewhere cover edge-case clamping and refusal behavior. Nothing here contradicts annotations, but the description itself doesn't disclose return shape, randomness guarantees, or side effects beyond what the schema provides.

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 compact, front-loaded with the core action and formats, and each sentence earns its place, including the rationale for why the tool exists. The category tag is small and useful rather than redundant.

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?

Given the fully documented schema and simple generation purpose, the description is largely complete. The main gap is the lack of an output schema or any statement about the return shape, especially whether count=1 returns a single value or an array. For a generator tool, that missing detail is a minor but real omission.

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 the parameters are already fully documented with defaults, ranges, conditional visibility, and enum labels. The tool description itself adds no parameter-level detail, which is acceptable given the complete schema, hence the baseline score of 3.

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 uses a specific verb–object pair ('Generate unique identifiers') and immediately enumerates the exact formats: UUID v4/v7, ULID, Nano ID, or random hex. This clearly distinguishes the tool from sibling generators like generate_hash or generate_password and leaves no ambiguity about what it produces.

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 gives explicit application context: use it when a workflow needs real identifiers rather than having a language model invent them. It does not enumerate sibling alternatives or say when not to use it, but the stated purpose is clear enough to guide selection.

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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