Archive a preference section. The section must be empty: delete its topics first, otherwise the request fails with 409.
| Name | Required | Description | Default |
|---|---|---|---|
| section_id | Yes | Id of the preference section to archive |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true and idempotentHint=true. The description adds valuable behavioral context beyond those annotations by stating that non-empty sections cause a 409 and that topics must be removed first. This operational constraint is not inferable from the annotations alone.
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 just two sentences with no filler. The core action is stated first, followed immediately by the critical precondition. Every word contributes to effective tool invocation.
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 mutation with no output schema, the description covers the essential operational context: what the tool does, what must be true before calling it, and what happens if that condition is violated. The annotations cover idempotency and destructiveness, so nothing important 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?
The schema already provides full coverage for the single parameter, section_id, with a clear description. The tool description does not add extra parameter-level detail, but the schema handles that sufficiently, so the baseline of 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 ('Archive') and a clear resource ('a preference section'), making the tool's function immediately identifiable. It also distinguishes itself from sibling tools like create_preference_section, replace_preference_section, and archive_preference_topic by naming the exact object being acted upon.
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 gives an explicit precondition: the section must be empty, and topics must be deleted first. It also specifies the failure mode (409) if this precondition is not met. It does not explicitly name an alternative tool such as archive_preference_topic, but the guidance is clear enough for an agent to understand the required workflow.
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.
