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da-okazaki

MCP FishAudio Server

by da-okazaki

fish_audio_tts

Convert text to speech using the Fish Audio TTS API, with options for voice models, streaming, and audio formats.

Instructions

Generate speech from text using Fish Audio TTS API

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
auto_playNoAutomatically play the generated audio (optional)
formatNoOutput audio format (optional)mp3
latencyNoLatency mode (optional)balanced
mp3_bitrateNoMP3 bitrate in kbps (optional)
normalizeNoEnable text normalization (optional)
output_pathNoCustom output file path (optional)
realtime_playNoEnable real-time audio playback during streaming (optional)
reference_idNoVoice model reference ID (optional)
reference_nameNoVoice model name to search for (optional)
reference_tagNoVoice model tag to search for (optional)
streamingNoEnable HTTP streaming mode (optional)
textYesText to convert to speech
websocket_streamingNoEnable WebSocket streaming mode (optional)
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool generates speech but doesn't cover critical aspects like rate limits, authentication requirements, error handling, or what the output looks like (e.g., audio file, URL, or stream). For a TTS tool with 13 parameters and no output schema, this is a significant gap.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with zero waste.

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 the complexity (13 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return value (e.g., audio data or file path), error conditions, or behavioral traits like streaming implications. For a TTS tool with many optional parameters, more context is needed to guide 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 description coverage is 100%, so the schema fully documents all 13 parameters. The description adds no additional parameter information beyond what's in the schema. According to the rules, when coverage is high (>80%), the baseline score is 3 even with no param info in the description.

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 the tool's purpose: 'Generate speech from text using Fish Audio TTS API.' It specifies the verb ('Generate speech') and resource ('from text'), but doesn't differentiate from its sibling tool 'fish_audio_list_references', which appears to serve a different function (listing references rather than generating speech).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool or any other TTS options, nor does it specify prerequisites or contexts for use. The agent must infer usage from the tool name and parameters alone.

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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