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Edge-TTS MCP Server

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Python
Hybrid

Edge-TTS MCP 服务器

模型上下文协议 (MCP) 服务器为利用 Microsoft Edge 的文本转语音功能的 AI 代理提供语音合成服务。

概述

该 MCP 服务器使用edge-tts库提供文本转语音功能。它被设计为一种工具，使人工智能代理能够以自然的声音做出回应。

Related MCP server: AivisSpeech MCP Server

功能

  • 文本到语音转换

  • 多种语音和语言支持

  • 调整音频速度和音调

  • 流音频数据

安装

pip install "edge_tts_mcp_server"

或者如果您想以开发模式安装：

git clone https://github.com/yuiseki/edge_tts_mcp_server.git
cd edge_tts_mcp_server
pip install -e .

如何使用

VS Code 中的示例设置

VS Code settings.json 中的设置示例：

"mcp": {
  "servers": {
    "edge-tts": {
      "command": "uv",
      "args": [
        "--directory",
        "C:\\Users\\__username__\\src\\edge_tts_mcp_server\\src\\edge_tts_mcp_server",
        "run",
        "server.py"
      ]
    }
  }
}

与 MCP Inspector 一起使用

作为标准 MCP 服务器运行：

mcp dev server.py

使用 uvx (uvicorn) 运行

如果您将其作为 uv 下的基于 FastAPI 的服务器运行：

uv --directory path/to/edge_tts_mcp_server/src/edge_tts_mcp_server run server.py

命令行选项：

edge-tts-mcp --host 0.0.0.0 --port 8080 --reload

API 端点

在 FastAPI 模式下运行时，以下端点可用：

  • / - API 信息

  • /health健康检查

  • /voices - 可用声音列表（可选择按?locale=ja-JP等进行过滤）

  • /mcp - MCP API 端点

执照

麻省理工学院

Available Tools

2 tools
list_voicesC

Get a list of available voices

ParametersJSON Schema
NameRequiredDescriptionDefault
localeNoOptional locale to filter voices (e.g., ja-JP, en-US)

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states what the tool does but doesn't disclose behavioral traits like whether it's read-only, requires authentication, has rate limits, returns paginated results, or what format the list is in. This leaves significant gaps for an agent to understand how to interact with it.

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, clear sentence with zero wasted words. It's appropriately sized for a simple tool and front-loaded with the essential information, making it easy to parse quickly.

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 no annotations, no output schema, and a simple input schema, the description is incomplete. It doesn't explain what the list contains (e.g., voice names, IDs, properties), how results are structured, or any behavioral aspects. For a tool that returns data, more context is needed to use it effectively.

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?

The schema description coverage is 100%, with the single parameter 'locale' documented in the schema. The description doesn't add any meaning beyond what the schema provides (e.g., it doesn't explain what happens if locale is omitted or provide examples beyond those in the schema). Baseline 3 is appropriate when the schema does the heavy lifting.

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 action ('Get a list') and resource ('available voices'), making the purpose immediately understandable. It doesn't differentiate from its sibling 'text_to_speech' tool, which performs a different function (synthesis vs listing), so it doesn't reach the highest score for sibling differentiation.

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 or in what context. While the sibling tool 'text_to_speech' is clearly for a different purpose, there's no explicit mention of when to use list_voices (e.g., for voice selection before synthesis) or any prerequisites.

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

text_to_speechC

Convert text to speech

ParametersJSON Schema
NameRequiredDescriptionDefault
textYesText to convert to speech
voiceNoVoice to use (default: ja-JP-NanamiNeural)ja-JP-NanamiNeural
rateNoSpeech rate (e.g., "+10%", "-10%")0%
volumeNoSpeech volume (e.g., "+10%", "-10%")0%
pitchNoSpeech pitch (e.g., "+10%", "-10%")0%
play_audioNoPlay the audio if true
use_default_playerNoUse default media player if true (use mpv if false)

TDQS

C2.9/5.0
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 but only states the basic function. It doesn't mention whether this is a read-only or destructive operation, what permissions might be required, rate limits, what format the output takes (audio file, stream, etc.), or any error conditions. For a tool with 7 parameters and no annotation coverage, this is insufficient.

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 extremely concise at just three words, with zero wasted language. It's front-loaded with the core function and contains no unnecessary elaboration. While this conciseness comes at the cost of completeness, the structure itself is optimal for its length.

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?

For a tool with 7 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (audio format, file location, success indicators), doesn't provide behavioral context, and offers no usage guidance. The 100% schema coverage helps with parameters, but the overall context for proper tool invocation is missing.

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?

The description adds no parameter information beyond what's already in the schema (which has 100% coverage). All 7 parameters are documented in the input schema with descriptions, defaults, and types. The description doesn't provide additional context about parameter interactions, validation rules, or examples of usage patterns.

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 'Convert text to speech' clearly states the tool's function with a specific verb ('convert') and resource ('text to speech'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its sibling 'list_voices' or explain how they differ in functionality.

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. There's no mention of the sibling tool 'list_voices' or any context about when text-to-speech conversion is appropriate versus listing available voices. The description lacks any usage context or prerequisites.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv0.1.0
    • First observedlist_voices
    • First observedtext_to_speech
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.2/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have completely distinct purposes: one lists available voices, the other converts text to speech. There is no overlap or ambiguity between them, making it easy for an agent to select the correct tool for each task.

Naming Consistency5/5

Both tools follow a consistent verb_noun naming pattern (list_voices, text_to_speech). The naming is clear, predictable, and adheres to a uniform style throughout the tool set.

Tool Count2/5

With only 2 tools, the server feels thin for a text-to-speech domain. While the tools cover core functionality, typical TTS systems might include additional operations like voice configuration, audio format settings, or batch processing, suggesting the scope is under-served.

Completeness3/5

The tools cover basic TTS operations (listing voices and converting text), but there are notable gaps. For example, there is no tool for configuring voice parameters (e.g., pitch, rate) or managing audio output formats, which could limit agent capabilities in more complex scenarios.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

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