get_scan_jobPoll an async batch scan started with scan_domains_batch: returns status (queued/running/succeeded/partial/failed), completed count, and per-domain results (domain, ok, score, grade) as they finish.
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes | The jobId returned by scan_domains_batch. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds useful behavioral context beyond annotations by specifying the polling nature, the possible statuses, and the per-domain result structure.
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, well-structured sentence that front-loads the action and uses a colon to efficiently enumerate return fields. Every word contributes 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?
For a simple polling tool with one parameter, no output schema, and strong annotations, the description fully covers the return structure and how it ties into scan_domains_batch. No critical information is missing.
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 the parameter fully described as 'The jobId returned by scan_domains_batch.' The description adds no new semantic detail about the parameter beyond what the schema provides, so baseline 3 is appropriate.
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 uses a specific verb ('Poll') and clearly identifies the resource ('async batch scan started with scan_domains_batch'). It distinguishes the tool from siblings by focusing on the batch scan job lifecycle and explicitly naming the initiating tool.
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 clearly implies when to use this tool: after calling scan_domains_batch, to poll the async job. It doesn't explicitly list exclusions or alternatives, but the context is specific enough to guide correct selection.
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.
Tools have distinct purposes overall, but some minor overlap exists between get_domain and get_deliverability_score, as well as between list_scans and get_scan_job. However, descriptions clarify the differences.
All tool names follow a clear verb_noun pattern (e.g., analyze_headers, check_blocklists, get_deliverability_score) with consistent snake_case, making it easy for an agent to predict tool names.
26 tools is slightly above the ideal range but appropriate for a comprehensive email deliverability service covering scanning, DNS fixes, DMARC, inbox placement, SNDS, alerts, and sharing. The count feels justified.
The tool surface covers core deliverability workflows: scanning, DNS fixes, DMARC, blocklists, inbox placement, SNDS, alerts, and sharing. Minor gaps exist (e.g., no explicit add domain tool), but the scan_domain tool covers that use case.