timezoneConvert a timestamp between IANA timezones with DST handled correctly. Returns local times, UTC offsets, weekday, and hour-of-day per zone. POST { time?, from?, to } — to is a zone or array (max 20). Price: $0.002/call with credits key; free demo without (2KB input cap).
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | IANA zone or array of zones (max 20) | |
| from | No | IANA zone the input wall-clock is in | |
| time | No | ISO string, epoch, or 'now' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description takes on full behavioral disclosure. It mentions DST correctness, return values, request format, and pricing/demo limits. However, it omits error behavior and authentication details beyond 'credits key', and does not explicitly state it's a read-only operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the core purpose, followed by return/request details and pricing. There is no filler; every sentence contributes useful information, making it efficiently structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a moderate tool with no output schema, the description adequately covers purpose, return fields, request format, and constraints. It lacks explicit response structure details, but the described return fields give a sufficient picture for an agent to invoke and interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with each parameter already well-described (e.g., 'IANA zone or array of zones (max 20)' for 'to'). The description repeats these constraints without adding new semantic detail, so it meets the baseline but does not exceed it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Convert a timestamp between IANA timezones with DST handled correctly.' It specifies the action, resource (timezones), and adds specifics like return fields (local times, UTC offsets, weekday, hour-of-day), distinguishing it from generic conversion tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (timezone conversion with DST handling) and outlines the request format. However, it does not explicitly state when not to use it or mention alternatives among siblings like 'convert' or 'cron', though the specificity reduces ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a unique, non-overlapping purpose. Even closely related tools like html_to_markdown and markdown_to_html are clearly inverses, and read_page combines fetching and conversion, so no two tools could be confused for the same task.
All names are lowercase with underscores, maintaining a consistent syntactic style. However, there is no strict verb-noun pattern: some names are nouns (cron, diff, qr, timezone), others are verbs (convert, extract), and word order varies (email_verify vs validate_ids), which is a minor deviation.
With 13 tools, the count is well within the ideal 3-15 range. Each tool provides a distinct paid utility, and none feel redundant or out of place for a general-purpose vending machine API.
The tool surface covers a broad range of common utilities—format conversion, validation, extraction, formatting, and time handling. There are minor gaps like missing YAML conversion or raw HTML fetching, but these are not critical for the server's stated purpose as a collection of paid utilities.