Check an async job started by verify_emails, find_email, or find_emails. Returns results if finished, otherwise the current status. Use the type shown in the submitting tool's response.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Job type: verify_bulk, find_single, or find_bulk | |
| job_id | Yes | Job ID from verify_emails / find_email / find_emails |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool returns results if finished or status otherwise, indicating a non-destructive, read-only behavior. No hidden side effects are implied.
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?
Two sentences with no wasted words. The main purpose is front-loaded, and guidance follows directly. Every sentence serves a clear purpose.
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?
Given the simplicity of the tool and good annotations (though none), the description covers primary functionality. It could mention that job_id is returned by the parent tools, but it's implied. No output schema exists, so explaining return values is 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?
Schema coverage is 100%, but the description adds value by connecting parameters to the submitting tools ('Job ID from verify_emails...' and 'type shown in the submitting tool's response'), which is not in the schema.
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 it checks async jobs started by verify_emails, find_email, or find_emails. It specifies what it returns (results or status), distinguishing it from the sibling tools that initiate jobs.
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?
It explicitly tells when to use the tool (after starting a job with those tools) and how to determine the 'type' parameter. It lacks explicit when-not-to-use instructions, but the context makes it clear there are no direct alternatives within this group.
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 unique, clearly defined purpose: find_email for single lookup, find_emails for bulk, verify_email for single verification, verify_emails for bulk, plus separate tools for checking credits and polling async jobs. No overlap.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., find_email, verify_emails, get_credits, get_job). The convention is uniform and predictable.
With 6 tools, the server is well-scoped, covering both single and batch operations for finding and verifying emails, plus needed auxiliary functions (credits, job polling). This is optimal for the domain.
The tool set provides a complete workflow: finding emails (single/bulk), verifying emails (single/bulk), checking credits, and handling async jobs. No obvious missing operations for the stated purpose.
