Skip to main content
Glama

text_to_speech

Convert text into spoken audio using OpenAI's TTS technology, with options for different voices, models, and audio formats. The generated audio can be saved to a file and optionally played automatically.

Instructions

Converts text into spoken audio using OpenAI TTS (default voice: alloy), saves it to a file, and optionally plays it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesThe text to synthesize into speech.
modelNoThe TTS model to use.tts-1
playNoWhether to automatically play the generated audio file.
response_formatNoThe format of the audio response.mp3
voiceNoOptional: The voice to use. Overrides the configured default (alloy).
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that audio is saved to a file and optionally played, which covers some output behavior, but lacks details on file location, naming, permissions, error handling, rate limits, or authentication needs. For a tool with no annotations, this leaves significant gaps in understanding its operational traits.

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 a single, well-structured sentence that efficiently conveys the core functionality, default settings, and optional features without any wasted words. It's front-loaded with the primary purpose and includes all necessary details concisely.

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?

Given the complexity (5 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and some behavioral aspects (saving and playing), but lacks details on output format, error handling, and other operational context that would be helpful for an agent to use it effectively without annotations.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning the default voice (alloy) and the optional play feature, but doesn't provide additional syntax, format, or usage context for parameters. This meets the baseline for high schema coverage.

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 the specific action ('Converts text into spoken audio'), identifies the resource ('using OpenAI TTS'), and provides implementation details ('saves it to a file, and optionally plays it'). It distinguishes itself by mentioning the default voice (alloy) and the optional play feature, which would be relevant if there were sibling tools.

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?

The description implies usage through the phrase 'optionally plays it,' suggesting when the play parameter might be used, but provides no explicit guidance on when to use this tool versus alternatives (though none are listed as siblings). There's no mention of prerequisites, limitations, or specific scenarios for choosing different models or voices.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/pinkpixel-dev/blabber-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server