media-mcp

generate_speech

Generate speech audio from text using Gemini TTS models. Supports single and multi-speaker modes with voice selection and style control.

Instructions

Generate speech audio from text using Gemini TTS models.

Supports single-speaker and multi-speaker modes with voice selection and natural language style control.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "generate_speech"
  }
}

Input Schema

Name	Required	Default
`text`	Yes
`model`	No	flash-tts
`speakers`	No
`voice_name`	No
`multi_speaker`	No
`style_instructions`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.7Jul 29, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It discloses modes, voice selection, and style control, which are key behavioral traits. However, it omits details like supported audio formats, latency, or that it uses specific models (though model enum is in schema). Missing some transparency.

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?

Two concise sentences, front-loaded with purpose, no unnecessary words. Efficiently conveys key capabilities.

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

Completeness2/5

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

Given 6 parameters, no output schema, and no annotations, the description is incomplete. It lacks details on the 'speakers' parameter structure, relationship between voice_name and speakers, and return format. Agent would need to infer or guess for effective use.

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 coverage is 0%, so description must add meaning. It hints at model, multi-speaker mode, voice selection, and style instructions, but does not explicitly map to parameters or explain complex ones like 'speakers' array. Adds some value but insufficient for full understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it generates speech audio from text using Gemini TTS models, and mentions modes and voice selection. It distinguishes from sibling tools (image, video, music) implicitly by modality, but does not explicitly differentiate.

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 context for when to use the tool: for text-to-speech with optional multi-speaker mode, voice selection, and style control. It does not explicitly state when not to use or list alternatives, which is acceptable given sibling tools cover different modalities.

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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