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mrfelfel

Taghvim

resolve_time

Resolve natural-language date expressions into deterministic ISO timestamps. Provides UTC, local, and precision details for relative dates such as 'tomorrow' or 'next Friday'.

Instructions

Resolve a natural-language date/time expression into a deterministic ISO timestamp. Use this whenever the user mentions relative dates ('tomorrow', 'next Friday', 'in two weeks', 'the first Monday of next month'). The LLM interprets the language; this tool performs the actual temporal computation. Returns structured result with UTC, local, and precision info.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
localeNoLocale for date interpretationen-US
timezoneNoIANA timezone for resolution, e.g. 'Europe/London'UTC
expressionYesNatural-language temporal expression, e.g. 'tomorrow at 3pm', 'next Friday', 'in 3 weeks'
reference_timeNoReference time as ISO 8601. Defaults to now. Use when relative to a specific date.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv3.0.1

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the transparency burden. It discloses that the tool is deterministic, returns a structured result with UTC, local, and precision info, and clarifies the division of labor between LLM and tool. It does not discuss edge cases or failure behavior, but for a pure computation tool this is reasonably transparent.

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 three sentences with no wasted words. It front-loads the core purpose, then gives usage guidance, then states the return shape. Every sentence contributes useful information.

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 four well-documented parameters and the absence of an output schema, the description covers the key invocation context: when to use it, what it does, and what kind of result it returns. It could mention precision semantics or invalid-expression behavior, but it is largely complete for selection and invocation.

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 schema already documents all parameters. The description reinforces the meaning of 'expression' with examples and mentions the output shape, but it does not add significant semantics beyond what the schema provides. Baseline 3 is appropriate.

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 states a specific verb and resource: 'Resolve a natural-language date/time expression into a deterministic ISO timestamp.' It also distinguishes the tool from sibling tools by clarifying that the LLM interprets language while the tool performs the actual temporal computation.

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 guidance on when to use the tool: whenever the user mentions relative dates like 'tomorrow' or 'next Friday'. It does not explicitly name alternatives or state when not to use it, but the 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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