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validate_email

Validate email deliverability with multi-layer checks: syntax, MX records, disposable and role address detection. Reduce bounce rates and spam complaints.

Instructions

Validate an email address for deliverability and quality.

Performs multi-layer validation: RFC-5322 syntax check, MX record lookup
to confirm the domain accepts mail, disposable/temporary domain detection
(against 50,000+ known throwaway providers), and role-address detection
(e.g. admin@, noreply@).

Use this before sending email campaigns, checking signups, or cleaning a
mailing list to reduce bounce rates and spam complaints.

Args:
    email: The email address to validate (e.g. 'user@example.com').

Returns:
    valid (bool): True if the address appears deliverable.
    reason: Human-readable explanation of the result.
    mx_found (bool): Whether the domain has working mail servers.
    is_disposable (bool): True if a known throwaway email provider.
    is_role_address (bool): True if a shared/role address (admin@, info@).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It details the multi-layer validation process and return values. Could be improved by mentioning network dependency or rate limits, but overall it discloses key behaviors.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections for checks, usage, args, and returns. Slightly lengthy but every sentence adds value. Front-loaded with the main purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with no output schema and no annotations, the description covers purpose, methodology, return fields, and use cases comprehensively. An agent has all needed information to use it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It explains the 'email' parameter with example and context, adding meaning beyond the schema's type-only definition. It could mention accepted formats or encoding but is sufficient.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it validates email for deliverability and quality, listing specific checks (RFC-5322, MX, disposable, role). This verb+resource description distinguishes it from sibling tools like dns_lookup or whois_lookup which are not email-specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly recommends use before email campaigns, signups, or list cleaning. While it doesn't explicitly state when not to use or mention alternatives, the context is clear enough for an agent to decide.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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