ielts-speaking-coach MCP Server
A local MCP server that enables IELTS speaking practice by managing training selections, session reviews, practice history, and dashboard data through tools like initialize_ielts_workspace, set_training_selection, save_session_review, and get_dashboard_data, all stored locally on the learner's computer.
README
IELTS Speaking Coach
A GitHub-ready IELTS Speaking desktop app, Codex plugin, and local MCP workflow:
- launch ChatGPT Voice from a Windows or macOS desktop dashboard;
- send the saved IELTS examiner prompt and capture visible conversation text;
- import an IELTS question bank;
- choose Part 1, Part 2, Part 3, or a full mock;
- create a 7-day, 14-day, or 30-day plan;
- generate the same structured review after every session;
- freely continue the last question, move to the next question, or select any historical session;
- save recommended targets and retrain one selected target through transfer;
- automatically ask for a delimited structured review and save it locally;
- review history, recurring issues, vocabulary, optional targets, and progress in the dashboard.
Privacy and question-bank content
Learner transcripts, recordings, reports, and login state stay on the local computer and are excluded from this repository. The repository ships only a small original sample question bank so the app can run after cloning. Bring your own licensed IELTS question bank through the import workflow; third-party or OCR-extracted commercial question banks are intentionally not included.
IELTS is a trademark of its respective owners. This independent project is not endorsed by or affiliated with the IELTS test partners. ChatGPT is a trademark of OpenAI; this project is not an official OpenAI product.
Desktop app
Run locally:
npm install
npm run desktop
The dashboard opens as a desktop window. Click 打开ChatGPT once and sign in yourself. The login is kept in an Electron persistent browser partition. Then select an IELTS question and click 保存并一键启动Voice. End Voice in ChatGPT when the practice is complete. The desktop bridge automatically generates and caches the review; click 同步复盘报告 to save it into the local learning record. If automatic generation does not start, click 补生成复盘报告 and then synchronize it.
After a requested review is saved, the desktop bridge immediately refreshes the latest report, practice history, issue archive, vocabulary archive, and optional retraining target without reloading the whole dashboard.
If the report already exists in the currently open ChatGPT conversation, click 同步复盘报告. The desktop bridge reads that conversation directly and falls back to the clipboard only when necessary. Slightly malformed JSON is repaired when possible before validation and synchronization. The synchronization button never generates a new report; 补生成复盘报告 is the explicit recovery action.
The desktop bridge attempts to locate ChatGPT's current web controls. If ChatGPT changes its interface, the prompt remains available and the learner may need to click Voice manually until selectors are updated.
Build a Windows installer:
npm run package:win
Build the macOS installers on a Mac:
npm run package:mac
The macOS build produces separate DMG and ZIP artifacts for Intel (x64) and
Apple silicon (arm64) Macs. A Windows .exe cannot run on macOS, and the
macOS artifacts must be built and tested on macOS. Public distribution also
requires Apple Developer ID signing and notarization; unsigned local builds may
be blocked by Gatekeeper.
Upgrade page
The dashboard includes a highlighted 功能升级 entry below the training navigation.
Set the personal-site destination in mcp/upgrade-page.json:
{
"websiteUrl": "https://your-domain.example/ielts-speaking-coach"
}
Only HTTPS destinations are accepted. In the packaged desktop app, the page opens in the learner's default browser so product copy, checkout, and delivery can be updated without changing the local dashboard workflow.
The plugin now includes a local STDIO MCP server. It stores selections, reports, history, and retraining targets on the learner's computer without a hosted backend.
Local MCP
Install dependencies once:
npm install
Run the automated MCP smoke test:
npm run test:mcp
The local MCP exposes:
initialize_ielts_workspaceopen_dashboardset_training_selectionget_training_contextsave_session_reviewlist_practice_historyget_dashboard_data
By default, learner data is stored under %LOCALAPPDATA%\IELTS Speaking Coach
on Windows and Electron's application data directory on macOS. Audio recording
is off by default. When the learner explicitly enables it, microphone-only
recordings are stored locally under the recordings folder and can be played or
deleted from the dashboard.
Button-first practice
Ask Codex to 打开雅思口语仪表盘. The open_dashboard
tool opens http://127.0.0.1:43127 in the learner's browser. The learner
chooses a route, Part, exact question, length, and optional single goal. In the
desktop app, the learner starts and ends the complete workflow with buttons.
Prototype boundary
The Skill and MCP do not add Voice by themselves. The optional desktop bridge controls the visible ChatGPT web interface and therefore depends on the learner's ChatGPT access and the current webpage structure. Audio is never stored unless the learner enables the local recording switch.
Try the dashboard
Open demo/dashboard.html in a browser. It contains sample data and demonstrates:
- choosing a Part and starting a Voice session;
- reviewing a standardized report;
- entering a one-target retraining session;
- browsing history, issue status, and vocabulary records.
Plugin structure
The installable skill is under skills/ielts-speaking-coach/. The plugin manifest is .codex-plugin/plugin.json.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。