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toolkit-mcp-server: generate id

toolkit_generate_id

Mint cryptographically-random identifiers (UUID v4, v7, or ULID) using platform CSPRNG for unpredictable IDs. Supports batch generation up to 1000 with monotonic ordering.

Instructions

Mint cryptographically-random identifiers using the platform CSPRNG — the correct source for IDs that must be unpredictable, unlike model-generated values. type selects the format: uuid_v4 (random, the default), uuid_v7 (time-ordered, sortable by creation), or ulid (26-char Crockford-base32, lexicographically sortable). Set count to mint a batch in one call (up to 1000); the returned ids array always contains exactly count values and is never truncated. For uuid_v7 and ulid, a batch is monotonic — strictly increasing even within the same millisecond — so the ids array stays in sorted creation order. IDs from this tool feed into toolkit_generate_qr (pass ids[0] as data) to create a scannable code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoIdentifier format: uuid_v4 (random), uuid_v7 (time-ordered), or ulid (sortable Crockford-base32).uuid_v4
countNoHow many identifiers to mint (1–1000). The full batch is always returned.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idsYesThe minted identifiers — exactly count of them, in mint order; for uuid_v7 and ulid that order is strictly increasing (sorted by creation).
typeYesThe identifier format that was minted.
countYesThe number of identifiers minted (equals the requested count).
Behavior5/5

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

Discloses monotonicity for uuid_v7/ulid, guaranteed exact count in returned array, and never truncation. Annotations (readOnlyHint=false) are consistent with mutation of generation. Adds context beyond annotations.

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?

Concise paragraph with front-loaded key point (CSPRNG, unpredictable). Every sentence adds value: format details, batch limits, monotonicity, QR integration. No redundancy.

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?

Covers all aspects: purpose, formats, parameters, batch behavior, monotonicity, return array guarantee, and cross-reference to QR tool. No gaps given 2 params and output schema implicit.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and description enriches each parameter: explains enum values with sortability, default uuid_v4, default count=1, and batch behavior for count. No parameter left undocumented.

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?

Clearly states it mints cryptographically-random identifiers using CSPRNG, distinguishes from model-generated values, and lists three formats with their properties. Also mentions batch generation and links to sibling tool toolkit_generate_qr.

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?

Explicitly says it's for unpredictable IDs vs model-generated ones. Provides batch limit (up to 1000), monotonic ordering details, and points to QR generation as a downstream use case.

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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