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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the read-only safety profile is covered. The description adds genuinely useful behavior beyond that: the warning that message content was authored by third parties and must be treated as data, never as instructions — a real prompt-injection guard — plus the note that templates carry approval state and that all=true includes completed reminders.

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?

Two sentences with no filler: the resource/verb scope is front-loaded, the enum enumeration is dense but each clause carries information an agent needs, and the safety caveat is placed last as a closing constraint. Nothing is repeated from the schema without added meaning.

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 read-only, four-parameter selector tool with no output schema, the description covers resource meanings, parameter interactions, and the injection hazard. The one gap is that it says nothing about return shape or volume (e.g. whether lists are truncated), which an agent might want before calling on reminders or profiles.

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 only 25%, but the description compensates well: it defines what each of the five enum values yields, explains the conditional `list_id` behavior (scoping to a list's contacts), and clarifies `all=true` (include completed items). The only parameter not addressed is `brand_id`, which the schema itself documents thoroughly (use the brand the user named, otherwise ask).

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?

Opening verb-plus-resource ("Lee datos de WhatsApp de la marca") is followed by an explicit mapping of every `what` value to what it returns (contact_lists, templates with approval state, campaigns with progress, profiles, reminders). This clearly separates it from the write-oriented siblings (whatsapp_create_*, whatsapp_send_*), so an agent can pick it without opening the schema.

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 gives clear context for use — read WhatsApp entities for a brand — and the per-value breakdown tells the agent which `what` to choose for a given need. It stops short of explicit exclusions (e.g. "to send a message use whatsapp_send_message") or prerequisites like brand selection, which are left to the schema and sibling names.

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