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ProofNexa B2B and Quant Risk Gate

verify_bulk_emails

Verify up to 50 B2B email addresses in bulk via live DNS MX record lookup, SPF/DMARC domain authentication posture, disposable email provider detection, and role-account classification. Returns a 0-100 deliverability score and cold-outreach safety verdict per email.

Input Schema

NameRequiredDescriptionDefault
emailsYesArray of B2B email addresses to verify (e.g., ["founder@stripe.com", "sales@linear.app"]).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / emails / description
      Previous value: -"List of email addresses to verify"New value: +"Array of B2B email addresses to verify (e.g., [\"founder@stripe.com\", \"sales@linear.app\"])."
  2. First observed

TDQS

A3.9/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden and does disclose substantial behavior: live DNS MX lookup, SPF/DMARC checks, disposable-provider detection, role-account classification, and per-email scoring/verdict output. It omits error handling, rate limits, and behavior for malformed or invalid email addresses, so it is not fully transparent.

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

Conciseness5/5

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

Two tightly written sentences with no wasted wording. The core capability and limit are front-loaded, followed by the return value, making it easy to scan.

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

Completeness4/5

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

Given one parameter and no output schema, the description explains the input limit, processing methods, and return values sufficiently for selection. It still lacks error/rate-limit behavior and explicit sibling differentiation, which would matter more if annotations or output schema were present.

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 description coverage is 100%, so the schema already documents the single emails parameter; baseline would be 3. The description adds meaningful constraints not in the schema: up to 50 addresses, B2B focus, and the verification methods applied to each address.

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

Purpose4/5

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

States a specific verb, resource, scope, and method: verify up to 50 B2B email addresses via live DNS MX lookup, SPF/DMARC posture, disposable detection, and role-account classification. It is clear enough to distinguish from a general domain-security audit, but it does not explicitly name or differentiate any sibling tool.

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

Usage Guidelines3/5

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

The description implies usage for bulk B2B email verification and cold-outreach safety, but it gives no explicit when-to-use/when-not guidance or named alternatives. An agent must infer the appropriate context from the description alone.

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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