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

The description discloses asynchronous execution, creation of a single bulk job, immediate job_id return, background mailbox probes, credit consumption per address, and result retrieval via get_verification. These details go far beyond the basic annotations and provide critical operational context for an agent.

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?

Four concise sentences cover purpose, async behavior, polling instructions, credit cost, and size limit without any fluff. The 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?

Despite having no output schema, the description explains the job_id return, how to obtain results via get_verification, and the result summary counts. The async flow, cost model, and limits are fully specified, making the tool self-sufficient for an agent to select and invoke correctly.

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 fully documents the sole 'emails' parameter with item type and maxItems=1000. The description reiterates the limit but adds no additional parameter-level semantics beyond what the schema provides, so the baseline 3 applies.

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 'Verify a large list of up to 1000 email addresses,' using a specific verb, resource, and explicit scope. It clearly distinguishes itself from siblings like verify_email by emphasizing bulk processing and immediate asynchronous execution.

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 explicitly instructs polling get_verification with the job_id for progress and directs users to the dashboard or bulk API for lists larger than 1000. This gives clear when-to-use and alternative guidance, leaving no ambiguity about the tool's role.

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