AivisSpeech MCP Server
Related Servers
Alternatives to AivisSpeech MCP Server
No user-submitted related servers found.
Related Servers
- AlicenseBqualityDmaintenanceA Model Context Protocol server that integrates with AivisSpeech to enable AI assistants to convert text to natural-sounding Japanese speech with customizable voice parameters.1358Apache 2.0
- AlicenseBqualityDmaintenanceA Model Context Protocol server that provides text-to-speech capabilities using the Kokoro TTS model, offering multiple voice options and customizable speech parameters.4321MIT
- AlicenseBqualityDmaintenanceA Model Context Protocol server that provides text-to-speech functionality for AI agents using Microsoft Edge's text-to-speech technology, supporting multiple voices, languages, and voice customization.28MIT
- AlicenseNot gradedqualityCmaintenanceA Model Context Protocol server that integrates high-quality text-to-speech capabilities with Claude Desktop and other MCP-compatible clients, supporting multiple voice options and audio formats.151MIT
- AlicenseBqualityFmaintenanceA server that enables Claude 3.7 and other AI agents to access VOICEVOX-compatible speech synthesis engines (AivisSpeech, VOICEVOX, COEIROINK) through the Model Context Protocol.112MIT
- AlicenseAqualityDmaintenanceA Model Context Protocol server for FlowSpeech text-to-speech. It lets MCP-compatible clients generate human-like audio with context-aware emotion control, pause control, multi-speaker dialogue, and 30+ available voices.313MIT
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'aivis-speech-synthesis' stands alone with a distinct purpose, so agents cannot misselect among multiple options.
A single tool inherently has consistent naming, as there are no other tools to compare it against. The name 'aivis-speech-synthesis' follows a clear pattern of domain-specific naming, and no inconsistencies can arise from a set of one.
A single tool for a speech synthesis server is too few for typical use cases, as it lacks essential operations like configuration, status checks, or batch processing. This minimal scope limits functionality and suggests an incomplete or overly simplistic implementation for the domain.
The tool set is severely incomplete for a speech synthesis domain, offering only synthesis without any supporting operations like listing voices, adjusting parameters, checking synthesis status, or handling errors. This creates significant gaps that will likely cause agent failures in real-world scenarios.