Covate

Covate

English | 简体中文 | 繁體中文

A context-aware Model Context Protocol (MCP) server that acts as a learning sidecar for AI coding assistants. It helps developers learn from AI-generated code changes through interactive quizzes and provides agents with a persistent project-specific debugging memory.

---

🌐 Resources

---

🚀 Why Use This?

---

☁️ Covate learning ledger (free)

The MCP server in this repo is free and open-source (MIT) — run it locally, no account required. The optional hosted [learning ledger](https://covate.org/dashboard) is free too: sign in with GitHub, then run COVATE_SYNC_URL=https://covate.org COVATE_SYNC_TOKEN= python -m covate.platform_sync from a project to push your local sessions up and review them in a browser:

• ☁️ Sync your learning sessions from any machine
• 📖 Every synced session, newest first, with its score
• 📊 Totals — sessions, questions, correct answers, running accuracy
• 🎯 The topics you answer worst, ranked
• 🔑 Your sync token — reveal or rotate it whenever you want

Not built yet, so not promised: progress-over-time charts, spaced-repetition study plans, team accounts. There is no paid tier and nothing to buy — the MCP works fully without the ledger, and the ledger costs nothing.

---

🔁 Spaced review (local, offline)

covate review keeps a small local ledger of concepts and when to ask about them again. It never talks to the network or to a model; your own agent writes the questions and publishes the page (for example with the FoldRender command line).

covate review page questions.json -o page.html   # questions -> a quiz page (answers marked)

foldrender publish page.html # your agent publishes it foldrender answers --json | covate review record # score the first answers covate review due # what to ask about again today covate review status # every concept + first-answer accuracy

Only the first pick on each question counts, so changing an answer after seeing the explanation cannot raise the score. (FoldRender shows the explanation during its few-second undo window, so an answer you take back inside that window does not count; one you leave alone does.) A new concept comes back after 1 day, then 7, then 30 days; a wrong answer sends it back to the 1-day step; passing all three means mastered. The ledger lives in the global Covate storage (override with COVATE_REVIEW_LEDGER). questions.json is {"title": ..., "questions": [{"concept", "question", "options": [...], "correct": <0-based index>, "explanation"?}]}.

In Claude Code, install the plugin to get the MCP server plus /covate:review and /covate:review-record, which walk your agent through the steps above (no hooks, nothing runs unless you call it):

/plugin marketplace add SunflowersLwtech/covate

/plugin install covate@covate

---

📦 Available Tools

Tool Details

🎓 learning_session - Interactive Learning Card

Trigger: User explicitly requests (e.g., "Quiz me", "Test my understanding")

Parameters:

Returns: {"status": "completed", "action": "HALT_GENERATION"}

🔍 debug_search - Search Debug History

Trigger: Auto-called when encountering errors (silent, no UI)

Parameters:

Returns: {"results": [...], "count": N}

📝 debug_record - Record Debug Experience

Trigger: Auto-called after fixing bugs (silent, background)

Parameters:

Returns: {"ok": true, "id": "..."}

📚 term_get - Get Programming Terms

Available Domains: programming_basics, data_structures, algorithms, software_design, web_development, version_control, testing, security, databases, devops

Parameters:

Returns: {"terms": [...], "count": N, "remaining": N}

---

🛠️ Installation

One-Line Install (Recommended)

Platform Command

macOS / Linux

curl -fsSL https://raw.githubusercontent.com/SunflowersLwtech/covate/main/scripts/install.sh | bash

Windows (PowerShell)

irm https://raw.githubusercontent.com/SunflowersLwtech/covate/main/scripts/install.ps1 | iex

The installer will:

1. Auto-detect your Python environment (uv → conda → venv)
2. Clone the repository to ~/covate
3. Create virtual environment and install dependencies
4. Print the exact command to configure your IDE

Manual Installation

Click to expand manual installation steps

Prerequisites: Python 3.11+ or uv

# 1. Clone the repository

git clone https://github.com/SunflowersLwtech/covate.git cd covate

2. Create virtual environment and install

Using uv (recommended)

uv venv --python 3.11 covate source covate/bin/activate # macOS/Linux

covate\Scripts\activate # Windows

uv pip install -e '.[dev]'

Or using standard venv

python -m venv covate source covate/bin/activate # macOS/Linux

covate\Scripts\activate # Windows

pip install -e '.[dev]'

Docker Installation

Click to expand Docker installation steps

Prerequisites: Docker installed on your system

# 1. Pull from Docker Hub

docker pull sunflowerslwtech/covate:latest

Or build locally

git clone https://github.com/SunflowersLwtech/covate.git cd covate docker build -t covate .

2. Run with Docker

docker run -i covate

3. Or use Docker Compose

docker-compose up -d

For detailed Docker usage, persistent storage, and Claude Desktop integration, see [DOCKER.md](DOCKER.md).

---

⚙️ IDE Configuration

Claude Code (CLI) — One Command Setup

After installation, configure your AI coding IDE to use this MCP server.

Claude Code

Option 1: CLI (Recommended)

# macOS / Linux

claude mcp add covate -- ~/covate/covate/bin/covate

Windows

claude mcp add covate -- %USERPROFILE%\covate\covate\Scripts\covate.exe

Option 2: Config File

Add to ~/.claude.json:

{

"mcpServers": { "covate": { "command": "~/covate/covate/bin/covate" } } }

For Windows:

{

"mcpServers": { "covate": { "command": "C:\\Users\\YourName\\covate\\covate\\Scripts\\covate.exe" } } }

Example paths:

• Unix (uv): ~/covate/covate/bin/covate
• Windows (uv): C:\\Users\\YourName\\covate\\covate\\Scripts\\covate.exe
• Windows (conda): C:\\Users\\YourName\\anaconda3\\envs\\covate\\Scripts\\covate.exe

Path breakdown (Unix example):

• ~/covate → repository directory
• covate → virtual environment directory created by uv/venv
• bin/covate → executable

Cursor

Add to Cursor MCP settings (Settings → MCP → Add Server):

{

"covate": { "command": "~/covate/covate/bin/covate" } }

For Windows:

{

"covate": { "command": "C:\\Users\\YourName\\covate\\covate\\Scripts\\covate.exe" } }

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{

"mcpServers": { "covate": { "command": "~/covate/covate/bin/covate" } } }

Docker Configuration

To use Docker with any MCP-compatible IDE:

{

"mcpServers": { "covate": { "command": "docker", "args": [ "run", "-i", "--rm", "-v", "/path/to/your/project:/workspace", "-w", "/workspace", "covate" ] } } }

See [DOCKER.md](DOCKER.md) for detailed Docker configuration examples for Claude Desktop, Cursor, and other IDEs.

Other IDEs

For any MCP-compatible IDE, use these settings:

• Command: /covate/bin/covate (or covate\Scripts\covate.exe on Windows)
• Transport: stdio

After configuration, restart your IDE.

Usage

Available Tools

| Tool | Trigger