tilwhen date engine

Parse a natural-language date question

parse_date_query

Read-onlyIdempotent

Parses queries like "90 business days from today in uganda" into a normalized operation, answers it, and returns the auditable receipt. Ambiguous dates (03/04/2027) return needsClarification instead of a guess.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`q`	Yes
`tz`	No	IANA timezone that defines "today" (e.g. Africa/Kampala). Default UTC.	UTC
`locale`	No	Resolves ambiguous slash dates; omit to receive clarification choices

Output Schema

TableJSON Schema

Name	Required	Description
`answer`	No	The calculated answer, absent when parsed is null
`parsed`	Yes	Normalized operation, or null when the query could not be understood confidently
`receipt`	No	Auditable receipt, absent when parsed is null
`warnings`	Yes	Machine-readable notes, e.g. unsupported_unit or time_ignored
`confidence`	Yes	1.0 is a clean grammar hit; below 0.5 the query is refused rather than guessed
`canonicalUrl`	No	tilwhen page that answers this query, to cite; null when no page covers it
`needsClarification`	No	Present when the query is genuinely ambiguous (e.g. 03/04/2027); offers explicit choices instead of a guess

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds value by disclosing that ambiguous dates return needsClarification and that output includes an auditable receipt. No contradictions.

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?

Two sentences, front-loaded with purpose and behavior. Every sentence adds distinct value. No unnecessary words.

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?

Given output schema exists (not shown but noted), description covers both normal outcome and the needsClarification edge case. Parameters are sufficiently contextualized. Complete for a parsing tool with 3 parameters.

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?

Schema coverage is 67% but description adds context: example queries for 'q', explains 'tz' as IANA timezone defining 'today', and clarifies that omitting 'locale' triggers clarification choices for ambiguous dates. This adds meaning beyond the schema.

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?

Description clearly states the tool parses natural-language date queries like '90 business days from today in uganda' into a normalized operation, answers it, and returns an auditable receipt. It distinguishes itself from sibling tools by handling broad queries rather than specific date arithmetic.

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?

Provides example queries and explains behavior for ambiguous dates (needsClarification). Does not explicitly state when to prefer this tool over siblings, but the sibling list implies specific arithmetic tools, so usage context is clear.

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

A4.1/5.0

Disambiguation5/5

Each tool targets a distinct date operation: calendar addition/subtraction, business days, days between, holidays, invoice due, next occurrence, natural language parsing, and an explainer. No two tools overlap in purpose; descriptions clearly differentiate them.

Naming Consistency5/5

All tool names follow a consistent pattern: lowercase with underscores, predominantly verb_noun (add_days, parse_date_query) or noun_phrase (business_days, invoice_due). The naming is uniform and predictable.

Tool Count5/5

With 9 tools, the server is well-scoped. Each tool addresses a core date calculation need without redundancy or bloat. The count is appropriate for a focused date engine.

Completeness5/5

The tool surface covers all essential date operations: calendar arithmetic, business days, holidays, payment terms, next occurrences, and natural language queries. No obvious gaps remain for common date calculations.

Resources

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