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voicemail_drops_generate_emma

Synthesize the drop's audio in Emma's voice (Felicia, ElevenLabs). Overwrites any existing audio. Server bills per character of input against the user's communications credit balance (50¢ per 1000 characters at standard tier; the response includes cost_cents). 2000-character cap (~2 minutes spoken). Inline emotion tags work — e.g. [warm], [chuckles], [whispers]…[/whispers]. The user can edit + re-generate later; script_text is persisted on the drop.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe script Felicia will speak. Max 2000 chars. Inline emotion tags supported.
dropIdYesUUID of the drop to synthesize audio for. From voicemail_drops_list / voicemail_drops_create.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations present, the description carries the full burden and does so impressively. It discloses the destructive overwrite behavior, billing model (50¢ per 1000 chars, cost_cents in response), character cap, inline emotion tags, persistence of script_text, and re-generation support. This leaves almost nothing about side effects or constraints to guesswork.

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?

Four tight sentences, each with a distinct purpose: core action, overwrite/cost, cap/tags, edit/persistence. The description is front-loaded with the essential verb and resource and contains zero filler or redundancy.

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?

For a tool with only 2 parameters washes some complexity (cost, overwrite, voice persona), the description is fully sufficient. It covers what the tool does, the prerequisites (existing dropId), the side effects, cost, constraints, and re-usability. There is no output schema and no annotations, but the description supplies all the operational knowledge an agent needs.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds meaningful context beyond the schema: it explains that 'text' is spoken in Emma/Felicia's voiceaine, ties text length to cost per character, and notes the overwrite/persistence effects. This enriches the agent's understanding of how parameters affect invocation.

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 action ('Synthesize the drop's audio') on a clear resource (the drop) using a specific voice ('Emma's voice (Felicia, ElevenLabs)'). This unambiguously distinguishes it from siblings like voicemail_drops_create, voicemail_drops_drop, and voicemail_drops_list, and even the name maps directly to the function.

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 implies clear usage: it synthesizes audio for an existing drop wether creating it first or regenerating later ('The user can edit + re-generate later'). The dropId source is given via 'From voicemail_drops_list / voicemail_drops_create', which guides sequencing. It does not explicitly enumerate when not to use it or name alternatives, but the context is unambiguous.

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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