verify_emailsVerify up to 50 email addresses at once and get a deliverability verdict for each, synchronously. Consumes 1 credit per address. For larger lists (up to 1000), use verify_list.
| Name | Required | Description | Default |
|---|---|---|---|
| emails | Yes | Email addresses to verify (1–50). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are sparse (readOnlyHint=false, destructiveHint=false, openWorldHint=true), and the description adds useful behavioral details: 'Consumes 1 credit per address' and 'synchronously.' This goes beyond what annotations convey, though it doesn't cover potential side effects or failure modes. The cost and synchronous nature are transparent and valuable.
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 sentences with no fluff. The first sentence states the core action and output; the second adds cost and an alternative for larger lists. Information is front-loaded and every sentence earns its place.
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 tool is simple (1 param, no output schema), and the description covers the main purpose, batch limit, cost, synchronous behavior, and alternative for larger lists. It implies the return value ('deliverability verdict for each') and is complete enough for straightforward use. It lacks explicit error handling or return format details, but these are not critical given the simplicity.
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 already provides full coverage for the single 'emails' parameter (description: 'Email addresses to verify (1–50)'), so the baseline is 3. The description adds semantic context by noting the 50-email upper bound and per-address credit consumption, which clarifies the parameter's cost and scale implications. This is a meaningful addition beyond the 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 clearly identifies the tool's function: 'Verify up to 50 email addresses at once and get a deliverability verdict for each, synchronously.' It specifies the resource (email batch) and the output (deliverability verdict), and distinguishes it from sibling verify_list by explicitly noting the size limit and alternative.
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 states when to use this tool: for batches up to 50, and provides an explicit alternative for larger lists: 'For larger lists (up to 1000), use verify_list.' This gives clear usage context and a specific alternative, satisfying the when/when-not/alternatives criterion.
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 targets a distinct function: single verification, synchronous batch, asynchronous bulk list, plus separate diagnostic checks (disposable, typo, domain, blacklist, header, SMTP code). The three verify tools are clearly differentiated by single vs. batch, sync vs. async, and size limits.
All tool names follow a consistent verb_noun pattern in snake_case: check_*, get_*, verify_*, generate_*, analyze_*, lookup_*. The batch variations (verify_emails, verify_list) are predictable extensions of verify_email.
12 tools is well within the ideal range for a focused email verification service. Each tool serves a clear purpose covering verification, diagnostics, DNS generation, and account management without unnecessary bulk.
The domain is well-covered: single/batch/large-list verification, common pre-check diagnostics, blacklist lookup, DNS record generation, and SMTP code explanations. Minor gaps include no history listing or batch job management beyond polling, but these are not core to the stated purpose.