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generate_audio

Convert text to speech or generate prompt-based audio via OpenAI, Gemini, fal, or Cloudflare, then save the audio file to disk and return its path.

Instructions

Text-to-speech (and prompt-based audio on fal). Saves the audio file to disk and returns its path.

provider (default: first configured of openai, gemini, fal, cloudflare):

  • openai: default gpt-4o-mini-tts, voice default alloy, mp3 output. options merged into the request (e.g. instructions, speed, response_format).

  • gemini: default gemini-3.8-flash-tts, voice default Kore (prebuilt voice name). options shallow-update generationConfig. Output is WAV.

  • fal: default fal-ai/elevenlabs/tts/turbo-v2.5. Input {text, voice} updated with options. Prompt-based fal audio models (music, sound effects) take options={"prompt": "..."}; any model id works.

  • cloudflare: default @cf/deepgram/aura-1. Body {text, speaker=voice} updated with options.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
modelNo
voiceNo
optionsNo
providerNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose the critical trait an agent needs: this writes an audio file to disk and returns its path rather than returning bytes. It also documents per-provider output formats (mp3, WAV) and how each backend's payload is mutated. It stops short of auth/error/rate-limit behavior.

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?

Purpose and side effect are front-loaded in one sentence, followed by a tight provider-by-provider list where every line adds actionable detail. There is no filler or repetition of the schema.

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

Completeness4/5

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

For a 5-parameter, no-output-schema tool at 0% schema coverage, this covers the essentials including return value and per-provider behavior. Marginal gaps remain: it does not clarify how the required `text` field interacts with fal prompt-based (music/SFX) models, nor what happens when no provider is configured.

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?

Schema coverage is 0%, and the description compensates thoroughly: it explains the default provider resolution order, the default model and voice per provider, and precisely how the opaque `options` object is applied (merged into the request, shallow-updates generationConfig, becomes {prompt} for fal prompt-based audio). That is meaning the schema cannot convey.

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?

Opens with a specific verb+resource ('Text-to-speech ... Saves the audio file to disk and returns its path'), naming both the operation and its side effect. It is trivially distinguishable from generate_image, start_video, and check_video by modality.

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 provider breakdown gives concrete selection guidance (which backend is default, what each accepts, what output format results), so an agent knows how to pick a route. What is missing is an explicit when-to-use/when-not statement or a named alternative for overlapping cases (e.g. fal prompt-based music/SFX vs speech).

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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