Cancel a running automation by its cancelation_token. This invokes a second ad-hoc automation with a single cancel step. The token must match the cancelation_token set when the original automation was started. Note: spelling is "cancelation_token" (single "l").
| Name | Required | Description | Default |
|---|---|---|---|
| cancelation_token | Yes | The cancelation_token that was set when the automation was originally invoked |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral details beyond annotations: it reveals that cancellation works by invoking a second ad-hoc automation with a single cancel step, which is non-obvious and useful. The spelling note also helps avoid invocation errors. Annotations already cover idempotence and non-destructive nature, so the bar is partially lowered.
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 concise and front-loaded with the core purpose. Four sentences are used, but each earns its place: the cancellation mechanism, the token matching prerequisite, and the spelling note are all relevant. Minor redundancy exists between 'cancelation_token' appearing in the first and third sentences.
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 one-parameter tool, the description is largely complete: it explains the action, the mechanism, and the token requirement. The lack of an output schema is acceptable since return values are not critical for invocation. It could briefly mention when not to use it, but this is not a significant gap.
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 input schema already documents the single parameter with 100% coverage. The description reinforces the matching requirement and adds a spelling clarification, but does not substantially expand parameter semantics beyond what the schema provides.
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 states an unambiguous action ('Cancel a running automation') and specifies the exact resource and identifier mechanism ('cancelation_token'). It clearly differentiates from sibling tools like cancel_journey and cancel_message by targeting automations specifically.
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?
The description provides clear context: use this tool to cancel a running automation, and the token must match the original cancelation_token. It does not explicitly exclude other scenarios or name alternatives, but the context is clear enough for 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 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.
