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Suggest Whatsapp Trigger Message

suggest_whatsapp_trigger_message

Have the AI write a WhatsApp message for a trigger from the page it follows, the app's brain and the record fields. Returns text to review; nothing is saved or sent.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "app_id": "<app_id>",
      "page_name": "<page_name>"
    },
    "name": "suggest_whatsapp_trigger_message"
  }
}

Input Schema

NameRequiredDescriptionDefault
app_idYesThe app the trigger belongs to.
fieldsNo
flow_idNo
page_nameYes
entity_nameNo
whatsapp_bot_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.2/5.0
Behavior3/5

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

Adds genuinely useful behavioral context beyond the annotations: the result is text to review and nothing is persisted or dispatched. Annotations only give generic hints (readOnlyHint=false, destructiveHint=false, non-idempotent) without explaining side effects, and the description does not address whether generation consumes credits or can fail on missing inputs. The 'nothing is saved' wording sits in mild tension with readOnlyHint=false but does not directly contradict it.

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

Conciseness4/5

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

Two tight sentences with the core action front-loaded and the non-persistence guarantee placed where it matters. The phrase 'the app's brain' is evocative but slightly imprecise for an agent parsing inputs.

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

Completeness3/5

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

With no output schema, the description appropriately notes that the return value is reviewable text, which covers the output gap. However, for a six-parameter tool at 17% schema coverage with no clarification of required vs optional inputs or failure conditions, the definition leaves meaningful holes.

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

Parameters2/5

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

Schema description coverage is only 17%, so the description must carry the parameter burden, and it only loosely maps inputs ('the page it follows', 'the app's brain', 'the record fields'). Three of six parameters (flow_id, entity_name, whatsapp_bot_id) get no explanation in either schema or description.

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

Purpose4/5

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

States a specific verb and resource ('have the AI write a WhatsApp message for a trigger') and scopes it as a drafting operation. The clause 'nothing is saved or sent' implicitly distinguishes it from create_whatsapp_trigger and send_whatsapp_trigger_test, though no sibling is named explicitly.

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

Usage Guidelines3/5

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

Usage is only implied: an agent can infer this is a pre-creation drafting step that should precede create_whatsapp_trigger, and that send_whatsapp_trigger_test is the alternative when actually sending. No explicit when-to-use, prerequisites, or named alternatives are given.

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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