get_job_resultsFetch the full per-address results of a completed bulk verification job by job_id (each email's verdict, recommendation, and reason) plus the valid/invalid/risky summary and credits used. The job must be 'completed' — check get_job_status first.
| 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?
With no annotations provided, the description carries full burden. It discloses the prerequisite for the job to be completed but does not specify error handling for invalid job_id, rate limits, or authentication needs. The read-only nature is implied but not explicit.
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 containing all essential information: what the tool returns and the prerequisite. No wasted words; front-loaded with the critical action.
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 single-parameter tool with no output schema, the description covers the main behavioral aspects (prerequisite, data returned). It lacks output format details, but given the context signals, it is adequate for an AI agent to understand the tool's purpose and usage.
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 a description for job_id. The description adds marginal value by linking the parameter to submit_bulk, but the schema already conveys its purpose. No additional formatting or constraints are explained.
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 'full per-address results of a completed bulk verification job', including specific data components like verdict, recommendation, reason, summary, and credits. It distinguishes itself from sibling tool get_job_status by focusing on results rather than status.
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 states the prerequisite that the job must be 'completed' and advises to check get_job_status first. However, it does not provide guidance on when not to use this tool or mention alternatives for incomplete or failed jobs.
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.