validate_idsValidate identifiers with real checksum math: credit card (Luhn + brand), IBAN (mod-97), EAN-13/UPC-A, ISBN-10/13, UUID (with version). POST { type, value } or { items: [{type, value}] } (max 50). Price: $0.003/call with credits key; free demo without (2KB input cap).
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | ||
| items | No | ||
| value | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses real checksum math, batch limits, authentication via credits key, pricing, and input caps. This goes beyond typical transparency, though it does not mention output format or error behavior.
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?
Three sentences, each earning its place: purpose, usage, and pricing/auth. No redundancy, and the critical information is front-loaded. Excellent structure.
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 captures usage constraints well, but because there is no output schema, it should at least hint at the response shape (e.g., validation result for each ID). Its omission leaves a notable gap for a validation tool, preventing a higher score.
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?
The schema has 0% description coverage, so the description compensates by explaining the relationship between type, value, and items (POST {type, value} or {items: [...]}). It clarifies the enum options and batch constraint, adding meaningful semantics not apparent from the bare schema.
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 uses a specific verb ('Validate') and resource ('identifiers') with a detailed list of supported types (credit card, IBAN, EAN-13/UPC-A, ISBN-10/13, UUID) and methods (Luhn, mod-97, etc.), making its purpose unmistakable and clearly distinct from sibling tools like convert or diff.
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?
It provides concrete usage context: POST with either a single object or items array (max 50), pricing with credits key, and free demo without. This tells the agent how to invoke it and under what constraints. It lacks explicit 'when not to use' guidance, but the tool's specificity makes alternatives apparent.
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.