get_usageReturn detailed usage statistics for the account over a period (day, week, or month): total credits used, emails processed, request counts broken down by endpoint and source, a daily breakdown, and recent activity.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max recent log entries to include (default 100, max 1000). | |
| period | No | Reporting period. Default 'month'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It indicates a read-only query, but does not explicitly state non-destructiveness, permissions, or rate limits. The listed output fields provide some behavioral context.
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?
The description is a single sentence that efficiently lists the key breakdowns. While not structured with bullets, it avoids redundancy and each element adds value.
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?
Without an output schema, the description adequately outlines the return fields (credits, emails, requests, daily breakdown, recent activity). It is sufficiently complete for a simple query tool, though it omits format details.
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?
Schema coverage is 100% with descriptions for both parameters. The description adds overall output context but does not enhance parameter-specific meaning beyond what the schema provides, meeting the baseline for high coverage.
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 states the verb 'Return' and resource 'detailed usage statistics' with specific breakdowns (credits, emails, requests, daily breakdown, recent activity). It distinguishes from related tools like get_credits and get_account.
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 implies usage for obtaining detailed usage stats but does not explicitly state when to use this tool versus alternatives (e.g., get_credits for credit balance only) or note any prerequisites.
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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Each tool has a clearly distinct purpose. Overlaps like verify_batch vs submit_bulk are explicitly differentiated by synchronous vs asynchronous behavior. Extraction, cleaning, verification, and management tools are well-separated.
All tool names follow a consistent verb_noun pattern (e.g., register_account, verify_email, get_job_status). There is no mixing of conventions or vague verbs.
15 tools cover the domain of email verification and account management without redundancy. Each tool fills a specific role—single verification, batch sync, bulk async, job polling, results retrieval, list cleaning, extraction, domain health, credit purchasing, and account management.
The tool set provides complete lifecycle coverage: account registration, usage tracking, credit purchase, email verification (single, batch, bulk with async), job management, list cleaning, email extraction, and domain health checks. No obvious gaps for the intended functionality.