cronParse a 5-field cron expression: get a plain-English explanation and the next N run times (UTC). POST { expression, count?, from? }. Price: $0.002/call with credits key; free demo without (2KB input cap).
| Name | Required | Description | Default |
|---|---|---|---|
| from | No | ISO date to project from | |
| count | No | ||
| expression | Yes | 5-field cron or @alias |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the HTTP method (POST), output (plain-English explanation and next N run times), timezone (UTC), pricing ($0.002/call with credits key), free demo without, and a 2KB input cap. This adds meaningful operational context, though it lacks error-handling or rate-limit details.
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?
Two concise sentences, front-loaded with the core purpose. Includes relevant operational details (pricing, input cap) without excess. No wasted words.
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?
Given the tool's simplicity and no output schema, the description adequately covers inputs, outputs, and constraints. It could mention error responses or additional output structure, but the high-level return description ('plain-English explanation and next N run times') is sufficient for basic usage.
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 67% (from and expression have descriptions; count lacks one). The description adds value by indicating the request body structure and clarifies that 'count' controls the 'N' in 'next N run times'. This partially compensates for the missing count description while not fully explaining all parameter nuances.
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 function: 'Parse a 5-field cron expression' and the outputs: 'plain-English explanation and the next N run times'. This is a specific verb+resource pairing that distinguishes it from siblings (convert, diff, etc.) which are unrelated.
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?
Provides clear context on how to use the tool (POST with { expression, count?, from? } and explains the purpose of decoding cron expressions). It does not explicitly mention when-not-to-use or alternatives, but the sibling tools are unrelated, so this is not a significant gap.
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.