list_notification_checks
List checks for a notification submission.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| submission_id | Yes | The submission ID for the checks resource | |
| notification_id | Yes | The notification template ID |
List checks for a notification submission.
| Name | Required | Description | Default |
|---|---|---|---|
| submission_id | Yes | The submission ID for the checks resource | |
| notification_id | Yes | The notification template ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already communicates that this is a safe read operation, and the description adds the scoping detail that it lists checks for a specific notification submission. It does not disclose return shape, pagination, or ordering, but for a simple read-only list operation the annotation plus scoping provides reasonable transparency.
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 front-loaded sentence with no filler or redundancy. It is concise and appropriately sized for a simple tool, though the ambiguity around 'checks' prevents it from being maximally helpful on its own.
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?
With no output schema, the description does not explain what a 'check' contains or what the return payload looks like, leaving some uncertainty. The operation is simple and the read-only annotation lowers risk, but the lack of any elaboration on the checks resource means the description is only minimally complete.
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 description coverage is 100%, and both parameters are documented with meaningful descriptions: submission_id is 'the submission ID for the checks resource' and notification_id is 'the notification template ID'. The description adds no additional parameter-level detail, so the schema carries the burden and the baseline of 3 applies.
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 ('List') and a specific resource ('checks for a notification submission'), which differentiates it from sibling tools like list_notifications. However, the term 'checks' is domain jargon and is not defined, so the purpose is clear but not fully self-explanatory.
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?
There is no guidance on when to use this tool versus alternatives such as list_notifications, list_notification_versions, or update_notification_checks. The intended context is implied only by the resource name and parameter schema, not explicitly stated.
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 are mostly organized as distinct resource/action pairs, but several clusters are easy to confuse: list subscription tools (add_subscribers_to_list vs bulk_subscribe_to_list vs subscribe_user_to_list), message vs message-content vs message-history retrieval, and the many journey/journey-template list/get tools. Detailed descriptions rescue most selections, but the sheer number of near-identical verb/resource names creates real misselection risk.
Almost all tools follow a snake_case verb_noun pattern (create_, get_, list_, replace_, send_, publish_, archive_). Minor deviations keep it from a perfect score: courier_installation_guide is noun-first, and add_bulk_users sits awkwardly next to the bulk_add_* family, but the overall convention is predictable and readable.
144 tools is an extreme working-set size for an agent to hold and choose from, far beyond the reasonable 3–15 range. Even for a broad platform like Courier, this should be split into focused sub-servers (templates, journeys, users, lists, preferences, etc.) to remain usable.
The surface is remarkably comprehensive, covering sending, templates, journeys, automations, users, tenants, lists, preferences, providers, routing, brands, audiences, translations, digests, bulk jobs, and audit events. Notable gaps exist—automation template CRUD and digest schedule management are missing—but most workflows can still be completed with workarounds.