email_verifyCheck an email address: RFC syntax, live MX records via DNS-over-HTTPS, and disposable-domain screening. POST { email } or { emails: [...] } (max 20). Price: $0.003/call with credits key; free demo without (2KB input cap).
| Name | Required | Description | Default |
|---|---|---|---|
| No | Single address | ||
| emails | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does disclose meaningful behavioral details: live network checks via DNS-over-HTTPS, pricing per call, the free demo mode, and the 2KB input cap. However, it does not disclose the response format, possible error behavior, or how authentication works beyond mentioning a 'credits key.'
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 two compact sentences with all information front-loaded. The first sentence states purpose and scope; the second covers request format, pricing, and demo limitations. No sentence is wasted.
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 simple read-type verification tool with no annotations and no output schema, the description covers purpose, input mechanics, and cost constraints well. However, it never explains what the tool returns after checking, which is a notable gap given no output schema exists to fill in that information.
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 only describes 'email' as a single address and 'emails' as an array with maxItems 20. The description adds significant semantics by specifying the POST body format, that the two parameters are alternatives, the maximum batch size, and the 2KB input cap, compensating for the 50% schema coverage.
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 the specific verb 'Check' and clearly identifies the resource (an email address) and the specific checks performed: RFC syntax, live MX records via DNS-over-HTTPS, and disposable-domain screening. This distinguishes it from all sibling tools, which are unrelated conversion/scheduling utilities.
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 gives concrete calling patterns (POST either { email } or { emails: [...] }) and constraints, making the intended usage immediately clear. It does not explicitly mention alternative tools or when-not-to-use, but none of the sibling tools overlap in purpose, so the lack of exclusions is acceptable.
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.