qrGenerate a QR code as standalone SVG. POST { text, ecLevel?: 'L'|'M'|'Q'|'H', margin?: 0-8, dark?, light? } (text max 2KB). Price: $0.005/call with credits key; free demo without (2KB input cap).
| Name | Required | Description | Default |
|---|---|---|---|
| dark | No | hex color | |
| text | Yes | ||
| light | No | hex color | |
| margin | No | ||
| ecLevel | No | M |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the HTTP method (POST), input size cap (2KB), output format (standalone SVG), and crucial operational details such as pricing ($0.005/call), authentication requirement (credits key), and a free demo option with a 2KB cap. This exceeds typical transparency, though it omits error/status codes and potential failure modes, so a perfect score is not warranted.
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 exceptionally concise: three sentences that front-load the core purpose, then present the payload shape, and finally pricing/auth. Every sentence adds essential information with zero wasted words, making it easy to scan and understand.
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?
The description covers the tool's purpose, input constraints, cost, and authentication, which is reasonable for a simple generator. However, it doesn't explain the semantics of ecLevel and dark/light, nor describe the response format beyond 'SVG', so an agent might misselect parameter values without additional knowledge. The lack of an output schema and annotations further raises the description's responsibility, and it only partially meets that bar.
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 description coverage is only 40% (dark and light as 'hex color'), and the description merely restates parameter constraints (e.g., text max 2KB, margin range) without explaining their meaning or purpose. It fails to clarify what ecLevel represents, what dark/light actually color, or how margin affects the output, leaving significant gaps for a 5-parameter tool.
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 opens with a specific verb ('Generate') and resource ('QR code') plus the output format ('standalone SVG'), making the tool's primary function unmistakable. It naturally distinguishes itself from sibling tools, none of which generate QR codes, so an agent can easily identify when this tool is relevant.
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 by showing the request shape (POST with the JSON payload) and notes the pricing/demo modes, but it doesn't explicitly state when to use this tool versus alternatives or mention exclusions. Since the purpose is self-evident and no sibling tool overlaps, the usage guidance is implied rather than explicit, which is a modest 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.