Mcp Server
What is Mcp Server
MCP_server is a speech analysis server that utilizes the Model Context Protocol (MCP) architecture for automatic analysis of spoken English content. It supports features such as speech-to-text conversion, grammar error analysis, vocabulary suggestions, logical suggestions, and sentence comparison.
Use cases
Use cases for MCP_server include: 1) Language learners receiving feedback on their spoken English, 2) Content creators enhancing their scripts or presentations, 3) Educators assessing student speech for grammar and vocabulary usage, and 4) Developers integrating speech analysis features into applications.
How to use
To use MCP_server, clone the repository from GitHub, set up a Python virtual environment, install the required dependencies, and then start the server using Uvicorn. You can trigger the analysis tools by sending POST requests to the server, as demonstrated in the provided MCPtest.json file.
Key features
Key features of MCP_server include: 1) Speech-to-text processing using OpenAI’s Whisper model, 2) Grammar error analysis, 3) Vocabulary suggestions for better word choices, 4) Logical suggestions for content structure and coherence, and 5) Sentence comparison to highlight discrepancies with the original text.
Where to use
MCP_server can be used in various fields such as education for language learning, content creation for improving written communication, and any application requiring speech analysis and feedback.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Mcp Server
MCP_server is a speech analysis server that utilizes the Model Context Protocol (MCP) architecture for automatic analysis of spoken English content. It supports features such as speech-to-text conversion, grammar error analysis, vocabulary suggestions, logical suggestions, and sentence comparison.
Use cases
Use cases for MCP_server include: 1) Language learners receiving feedback on their spoken English, 2) Content creators enhancing their scripts or presentations, 3) Educators assessing student speech for grammar and vocabulary usage, and 4) Developers integrating speech analysis features into applications.
How to use
To use MCP_server, clone the repository from GitHub, set up a Python virtual environment, install the required dependencies, and then start the server using Uvicorn. You can trigger the analysis tools by sending POST requests to the server, as demonstrated in the provided MCPtest.json file.
Key features
Key features of MCP_server include: 1) Speech-to-text processing using OpenAI’s Whisper model, 2) Grammar error analysis, 3) Vocabulary suggestions for better word choices, 4) Logical suggestions for content structure and coherence, and 5) Sentence comparison to highlight discrepancies with the original text.
Where to use
MCP_server can be used in various fields such as education for language learning, content creation for improving written communication, and any application requiring speech analysis and feedback.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
Speech Analysis MCP Server
本專案是一個語音分析伺服器，使用MCP（Model Context Protocol）架構，用於自動分析英文口說內容。支援語音轉文字、文法錯誤分析、詞彙建議、邏輯建議及原句比對等功能。
專案結構
. ├── models/ # 工具呼叫封裝邏輯（如 tool_call.py） ├── myenv/ # Python 虛擬環境（不建議提交） ├── speech_audio/ # 參考或教學音檔 ├── tools/ # MCP 工具集合 │ ├── compareSentence.py # 比對原文與口說差異 │ ├── grammar.py # 文法分析工具 │ ├── logic.py # 內容邏輯建議工具 │ ├── vocab.py # 詞彙建議工具 │ └── whisper.py # 語音轉文字處理 ├── user_audio/ # 使用者上傳的語音檔 ├── main.py # MCP server 主程式 ├── MCPtest.json # MCP 測試請求樣板 ├── requirements.txt # Python 套件安裝清單 └── README.md # 專案說明文件
功能說明
- 語音轉文字：使用 OpenAI Whisper 模型處理語音檔
- 文法檢查：分析文法錯誤
- 詞彙建議：提供更佳詞彙選擇
- 邏輯建議：回饋語句內容結構與邏輯
- 句子比對：標示與原文不一致之處
安裝步驟
-
下載專案：
git clone https://github.com/YunYue1029/MCP_server cd MCP_server -
建立虛擬環境並安裝依賴：
python -m venv myenv source myenv/bin/activate # Windows: myenv\Scripts\activate pip install -r requirements.txt -
啟動 MCP server：
uvicorn main:app --reload
API & 測試方式
可使用 main.py 搭配 POST 請求來觸發工具分析，範例請參考 MCPtest.json。
音檔支援格式
.mp3,.wav- 測試音檔可放置於
user_audio/目錄
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
