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bulk_replace_user_preferences

DestructiveIdempotent

Replace a user's complete set of preference overrides in one request. The topics in the body become the recipient's entire override set: listed topics are created or updated, and every existing override not included is reset to its topic default. An empty topics array clears all overrides. Validation-atomic (all-or-nothing).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicsYesThe complete set of topic overrides. An empty array resets every existing override.
user_idYesThe user ID
tenant_idNoScope the replacement to a specific tenant context

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses destructive behavior in detail: omitted overrides are reset to defaults and an empty array clears all overrides, going beyond the annotations' destructiveHint=true. It also adds the validation-atomicity trait, which annotations do not convey, and there is no contradiction with readOnlyHint=false or idempotentHint=true.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences plus one atomicity tag; each sentence carries a distinct fact (scope, reset behavior, empty-array behavior, transactional guarantee). The purpose is front-loaded and there is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is complete enough for a destructive bulk replace: it explains the full replacement contract, the empty-array edge case, and atomicity, while annotations and full schema coverage handle safety and parameter meaning. It does not mention an alternative for partial updates or describe the response/return value, but given no output schema and a clear mutation contract, those are minor gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already documents all parameters (100% coverage), so the description does not need to repeat definitions. It adds meaningful behavior for `topics`—listed topics are created or updated, omitted ones are reset to defaults—which goes slightly beyond the schema's 'complete set' summary. Other parameters like `user_id` and `tenant_id` are adequately covered by schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and object ('Replace a user's complete set of preference overrides'), and the second sentence clarifies the exact scope of replacement ('entire override set', 'every existing override not included is reset'). This clearly distinguishes the full-replace semantics from partial-update siblings even without naming them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description makes the use case explicit: use this tool when the caller wants to set a user's full override set in one atomic request. It implies that partial updates or single-topic changes belong elsewhere, though it does not explicitly name alternatives like bulk_update_user_preferences, so the routing guidance is slightly implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.1/5.0
Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness4/5

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.