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generate_speech

Convert text into expressive character dialogue and voice acting, with emotional profiles and fine-tuned delivery settings. Runs asynchronously, returning job IDs for polling.

Instructions

Generate spoken dialogue / character voice acting.

Returns a job_id immediately; poll with check_jobs([job_id]) for the file path. Fire several calls to voice a whole scene without waiting.

Two ways to shape delivery, and they combine:

  • A profile — a saved persona/mood preset. Built-ins: neutral, narrator, calm, whisper, sad, angry, excited, shout (see list_profiles). Save your own with save_profile. A profile just presets the knobs below.

  • The precision knobs — any of these override the profile for this one line. Emotion can also be written inline as Eleven v3 audio tags, e.g. "[whispers] they're coming... [shouts] run!".

Args: text: The line(s) to speak, optionally with inline v3 audio tags. voice: An ElevenLabs voice name (see list_voices) or a voice id. Overrides any voice pinned by the profile. Omit for the default voice. profile: A saved/built-in profile name to preset delivery. model: Model id (default eleven_v3 for expressive character acting). stability: v3 delivery mode — "creative" (most expressive/tag-responsive), "natural" (balanced), or "robust" (locked-in) — or a 0-1 float. style: 0-1, amplifies the speaker's stylistic character (default 0). speed: Playback speed, 1.0 = normal (roughly 0.7-1.2 is usable). similarity_boost: 0-1, adherence to the reference voice (default 0.75). use_speaker_boost: Boost speaker similarity (slightly higher latency). seed: Fix sampling (0-4294967295) for more reproducible takes. Best-effort, NOT bit-exact (especially on v3) — a character's identity comes from its voice and settings, not the seed. previous_text / next_text: Surrounding lines, for prosody continuity in a scene. language_code: ISO 639-1 code to pin the language (not for multilingual_v2). with_timestamps: Also return per-character alignment (word/char timings) as a sidecar JSON next to the audio — for lip-sync, captions, and cut timing. variants: Generate N takes of this line in parallel (2-8) so a human can pick the best — v3 varies run-to-run. Returns {"job_ids": [...]} instead of one job_id. pronunciation: One or more pronunciation-dictionary names (or ids) to apply so lore names/terms are said a fixed way — up to 3. Create one with create_pronunciation_dictionary. engine: Provider override. Defaults to ElevenLabs for speech. format: Output format ("mp3" default, "wav"/PCM where the tier supports it).

Returns: {"job_id": ..., "status": "queued", "engine": ...} or {"error": ...}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
textYes
modelNo
speedNo
styleNo
voiceNo
engineNo
formatNo
profileNo
variantsNo
next_textNo
stabilityNo
language_codeNo
previous_textNo
pronunciationNo
with_timestampsNo
similarity_boostNo
use_speaker_boostNo
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It transparently discloses key behaviors: immediate job_id return and polling via check_jobs, seed non-determinism ('NOT bit-exact'), variants behavior returning job_ids, with_timestamps sidecar, and profile override logic. These details set accurate expectations for the agent.

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?

The description is long but densely informative. It opens with the async workflow, then provides a structured Args list mapping to all 18 parameters, followed by a clear Returns contract. There is no redundant filler; every sentence serves the agent's invocation decision.

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?

Given the tool's complexity (18 params, no annotations, no output schema), the description covers purpose, workflow, parameter semantics, return shape, and caveats such as seed reproducibility and v3 expressiveness. It is complete enough for an agent to use effectively without additional context.

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

Parameters5/5

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

With 0% schema description coverage, the description fully compensates by explaining every parameter's meaning, defaults, and interplay. For example, it clarifies that profile presets are overridden by precision knobs, explains stability mode options and semantics, and describes pronunciation dictionary usage. This adds substantial value beyond bare property names.

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 clearly states 'Generate spoken dialogue / character voice acting', which is a specific verb+resource combination. It distinguishes itself from sibling tools like generate_music and generate_sfx by focusing on speech. The async nature and workflow further differentiate it from simpler speech tools.

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 provides clear usage context: it is for generating dialogue/voice acting asynchronously, encourages firing multiple calls for a scene, and references related tools (check_jobs, list_profiles, save_profile, list_voices). However, it does not explicitly state when not to use this tool or compare it directly to alternatives like generate_dialogue or speak, so it misses the explicit exclusion criteria for a 5.

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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