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Qualisend Email Verification

Verify a large list of email addresses

verify_list

Verify a large list of up to 1000 email addresses. This runs ASYNCHRONOUSLY: it creates ONE bulk job, returns a job_id immediately, and the mailbox probes run in the background. Poll get_verification with the job_id for progress and a result summary (counts by verdict). Consumes 1 credit per address. For lists larger than 1000, use the Qualisend dashboard or bulk API.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailsYesEmail addresses to verify (1-1000).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / emails / description
      Previous value: -"Email addresses to verify (1–1000)."New value: +"Email addresses to verify (1-1000)."
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Adds substantial behavior beyond the annotations: it runs asynchronously, creates exactly ONE bulk job, returns a job_id immediately while probes run in the background, and consumes 1 credit per address. That credit cost and the one-job-per-call guarantee are exactly the kind of context annotations (readOnlyHint=false, openWorldHint=true, destructiveHint=false) cannot convey.

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?

Four tight sentences, front-loaded with the async behavior and job_id return, then polling, cost, and the overflow path. No sentence is filler.

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?

Without an output schema, the description still tells the agent what comes back immediately (job_id), where the actual results live (get_verification, counts by verdict), cost, and the over-limit fallback. Nothing needed to invoke it correctly is missing.

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

Parameters3/5

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

Only one parameter and the schema already documents it at 100% coverage (array of strings, 1-1000, matching maxItems). The description restates the 1000 bound and adds the per-address credit cost, but adds no new syntax or format detail; baseline 3 applies when the schema does the heavy lifting.

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?

States a specific verb and resource with scope: 'Verify a large list of up to 1000 email addresses.' The word 'large' plus the 1000 cap distinguishes it from the single-address verify_email and the smaller verify_emails siblings.

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

Usage Guidelines5/5

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

Explicit routing: use this for lists up to 1000, 'For lists larger than 1000, use the Qualisend dashboard or bulk API.' It also names the follow-up tool (get_verification) and the condition that triggers it (polling for progress).

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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