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List AI Voice-Agent Presets

wa_ai_list_presets
Read-only

List the AI voice-agent presets of this workspace. A preset pairs a voice agent with one of your WhatsApp numbers (the line it calls from) and default variables. Use a preset's name with wa_ai_call_start; only presets with ready=true can call now (not_ready_reason says why otherwise). expected_variables are the variables the agent's prompt uses — pass them to wa_ai_call_start.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
presetsYesyour presets; call wa_ai_call_start with a preset whose ready is true
refusalNopresent only when the request was declined

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedOutput schema / properties / refusal / properties / account_status
      Added value: +{
+  "description": "when the number is not working: its status — starting, not_running, whatsapp_down, suspended, pairing, unlinked or locked",
+  "type": "string"
+}
    • addedOutput schema / properties / refusal / properties / account_status_reason
      Added value: +{
+  "description": "the detail of account_status, e.g. no_egress, stopped, no_plan",
+  "type": "string"
+}
    • addedOutput schema / properties / refusal / properties / since
      Added value: +{
+  "description": "when the number stopped working, RFC3339, when known",
+  "type": "string"
+}
  2. Changed3 schema fields changed
    • addedOutput schema / properties / presets / items / properties / ring_delay_secs
      Added value: +{
+  "type": "integer"
+}
    • addedOutput schema / properties / presets / items / properties / start_delay_secs
      Added value: +{
+  "type": "integer"
+}
    • changedOutput schema / properties / presets / items / required
      Previous value: -[
-  "id",
-  "name",
-  "description",
-  "provider",
-  "agent_id",
-  "agent_name",
-  "account_id",
-  "line",
-  "default_variables",
-  "expected_variables",
-  "enabled",
-  "answers_inbound",
-  "ready"
-]New value: +[
+  "id",
+  "name",
+  "description",
+  "provider",
+  "agent_id",
+  "agent_name",
+  "account_id",
+  "line",
+  "default_variables",
+  "expected_variables",
+  "enabled",
+  "answers_inbound",
+  "ring_delay_secs",
+  "start_delay_secs",
+  "ready"
+]
  3. Changed2 schema fields changed
    • addedOutput schema / properties / presets / items / properties / answers_inbound
      Added value: +{
+  "type": "boolean"
+}
    • changedOutput schema / properties / presets / items / required
      Previous value: -[
-  "id",
-  "name",
-  "description",
-  "provider",
-  "agent_id",
-  "agent_name",
-  "account_id",
-  "line",
-  "default_variables",
-  "expected_variables",
-  "enabled",
-  "ready"
-]New value: +[
+  "id",
+  "name",
+  "description",
+  "provider",
+  "agent_id",
+  "agent_name",
+  "account_id",
+  "line",
+  "default_variables",
+  "expected_variables",
+  "enabled",
+  "answers_inbound",
+  "ready"
+]
  4. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, but the description adds real semantic context: what a preset bundles, the meaning of the ready flag, that not_ready_reason explains failures, and what expected_variables represent. This is well beyond the annotation coverage and directly informs correct downstream use.

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 sentences, front-loaded with the purpose, then the concept, then the actionable hooks into wa_ai_call_start. Every sentence carries information an agent needs and none is redundant.

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

Completeness5/5

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

An output schema exists, so return values need not be re-documented, yet the description still clarifies the key output fields (ready, not_ready_reason, expected_variables) that drive correct behavior. Nothing essential for invoking this tool is missing.

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 tool takes zero parameters, so there is nothing for the description to disambiguate. Baseline for a no-parameter tool is 4, and the description reasonably spends its space on output field semantics instead.

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?

States a specific verb ('List') and resource ('AI voice-agent presets of this workspace') and then defines what a preset actually is, so the agent understands the concept rather than just the name. It is clearly distinguishable from siblings like wa_ai_call_start, whose relationship to presets it explains.

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?

It explicitly routes the agent forward: 'Use a preset's name with wa_ai_call_start' and warns that 'only presets with ready=true can call now'. That is clear context for when and how to consume this tool's output, though it stops short of an explicit when-not-to-use statement.

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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