english-tutor-mcp
Enables English language practice by silently logging messages and providing reports on recurring grammar patterns without interrupting conversation.
README
english-tutor-mcp
Practice English by just talking to your AI assistant — and get a daily report of your recurring grammar patterns, without ever being corrected mid-conversation.
Works with any MCP client that runs local (stdio) servers: Claude Code, Claude Desktop, Codex CLI, Cursor, and more.
- While you chat — every English message you write is silently logged, verbatim, into a local SQLite database. No corrections, no interruptions: fluency practice stays fluent.
- When you ask —
/reportanalyzes everything since your last report as a whole and saves a markdown report. You get patterns ("drops articles before abstract nouns — 6 times"), not one-off nitpicks. - Local-only — no API key, no account, no hosted server. Everything lives in
~/.english-tutor/on your machine.
Quick start (Claude Code)
claude mcp add english-tutor -s user -- npx -y english-tutor-mcp
Then just talk to your assistant in English — anywhere, about anything. Every English message is collected quietly. Whenever you want feedback (tomorrow, or after a few days):
/mcp__english-tutor__report
It analyzes everything since your last report and saves it to ~/.english-tutor/reports/. Forgot for three days? It catches up on all three at once.
Commands
| Prompt | What it does |
|---|---|
/report |
Analyzes all conversations not yet analyzed — however many days have piled up — and saves a markdown report. Run it in a fresh session |
/report [days] |
Retrospective mode: re-analyzes the last N days as one window (e.g. 7 for a weekly review), surfacing patterns too rare to show up in a single day |
/weak-points [days] |
Shows persistent weak points aggregated from saved reports (default 30 days). Read-only |
/language [lang] |
Sets the language your feedback is written in, persisted across sessions. By default it is inferred — reports arrive in your native language, quotes stay in English |
Other clients
<details> <summary><b>Claude Desktop</b> — <code>claude_desktop_config.json</code></summary>
{
"mcpServers": {
"english-tutor": {
"command": "npx",
"args": ["-y", "english-tutor-mcp"]
}
}
}
</details>
<details> <summary><b>Cursor</b> — <code>.cursor/mcp.json</code></summary>
{
"mcpServers": {
"english-tutor": {
"command": "npx",
"args": ["-y", "english-tutor-mcp"]
}
}
}
</details>
<details> <summary><b>Codex CLI</b> — <code>~/.codex/config.toml</code></summary>
[mcp_servers.english-tutor]
command = "npx"
args = ["-y", "english-tutor-mcp"]
</details>
ChatGPT web/desktop app: not supported yet — ChatGPT connectors only reach remote MCP servers over HTTPS, while this server runs locally over stdio to keep your data on your machine. If you use the ChatGPT ecosystem, Codex CLI works today. A remote-capable HTTP mode is under consideration for v2.
How it works
- Capture, don't judge. During conversation the model only calls
log_utterance, storing your message exactly as you typed it — the errors are the data. Real-time correction would both break fluency practice and destroy the samples. - Patterns, not sentences. Analysis always runs over a whole day's corpus at once. One sentence shows a missing article; forty sentences show where you drop articles. Findings below 3 occurrences are discarded.
- The host model does the analysis. The server is deliberately just SQLite CRUD — that's why no API key is needed and installation is one line. The AI subscription you already have does the thinking.
- Nothing is ever missed. Sessions queue up unanalyzed until your next
/report, which catches them all up in one pass — skipping days costs you nothing.
Data
| Path | Contents |
|---|---|
~/.english-tutor/db.sqlite |
sessions, verbatim utterances, findings, report index |
~/.english-tutor/reports/YYYY-MM-DD.md |
daily markdown reports |
Relocate everything with ENGLISH_TUTOR_DB=/path/to/db.sqlite. Delete the directory to erase all data — nothing leaves your machine.
License
MIT License
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。