get_job_statusFetch the current status and progress of a bulk verification job by its job_id (status, percent progress, processed count, and a valid/invalid/risky summary). Poll this after submit_bulk until status is 'completed', then call get_job_results.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | The job_id returned by submit_bulk. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that the tool is for polling and returns specific progress fields. While not exhaustive (e.g., missing rate limits or error states), it adequately describes the read-only polling behavior without annotations.
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?
Single sentence with parenthetical list, no redundant words. Front-loads purpose and usage. Highly efficient.
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?
For a simple status polling tool with one parameter and no output schema, the description covers the lifecycle (when to poll, what it returns, next step). Minor gap: no mention of error handling, but sufficient.
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 covers 100% of parameters, but the description adds value by specifying that job_id comes from submit_bulk, providing important context beyond the schema definition.
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 'Fetch' and the resource 'current status and progress of a bulk verification job'. It distinguishes from sibling tools like submit_bulk and get_job_results by specifying the context and next steps.
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?
Explicitly instructs when to use: 'Poll this after submit_bulk until status is "completed", then call get_job_results.' This provides clear guidance on sequencing and conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.