MCP Creator Growth

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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.

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🌐 Resources

Resource	Description
Glama MCP Marketplace	Official MCP server listing with installation guides
DeepWiki Documentation	AI-generated deep analysis of the codebase
GitHub Repository	Source code, issues, and contributions

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🚀 Why Use This?

For	Benefit
Developers	Don't just accept AI code—understand it. Request a quiz to verify your grasp of the logic, security, or performance implications.
AI Agents	Stop solving the same bug twice. The server quietly records debugging solutions and retrieves them automatically when similar errors occur.

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📦 Available Tools

Tool	Type	Description
learning_session	🎓 Interactive	Opens a WebUI quiz based on recent code changes. Blocks until user completes learning.
debug_search	🔍 Silent RAG	Searches project debug history for relevant past solutions. Auto-triggered on errors.
debug_record	📝 Silent	Records debugging experiences to project knowledge base. Auto-triggered after fixes.
term_get	📚 Reference	Fetches programming terms/concepts. Tracks shown terms to avoid repetition.

Tool Details

🎓 learning_session - Interactive Learning Card

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

Parameters:

Parameter	Type	Default	Description
project_directory	string	"."	Project directory path
summary	string	—	Structured summary of Agent's actions
reasoning	object	null	5-Why reasoning (goal, trigger, mechanism, alternatives, risks)
quizzes	array	auto-generated	3 quiz questions with options, answer, explanation
focus_areas	array	["logic"]	Focus areas: logic, security, performance, architecture, syntax
timeout	int	600	Timeout in seconds (60-7200)

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

🔍 debug_search - Search Debug History

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

Parameters:

Parameter	Type	Default	Description
query	string	—	Error message or description to search
project_directory	string	"."	Project directory path
error_type	string	null	Filter by error type (e.g., ImportError)
tags	array	null	Filter by tags
limit	int	5	Maximum results (1-20)

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

📝 debug_record - Record Debug Experience

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

Parameters:

Parameter	Type	Default	Description
context	object	—	Error context: {error_type, error_message, file, line}
cause	string	—	Root cause analysis
solution	string	—	Solution that worked
project_directory	string	"."	Project directory path
tags	array	null	Tags for categorization

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:

Parameter	Type	Default	Description
project_directory	string	"."	Project directory path
count	int	3	Number of terms (1-5)
domain	string	null	Filter by domain

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

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🛠️ Installation

One-Line Install (Recommended)

Platform Command

macOS / Linux

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

Windows (PowerShell)

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

The installer will:

1. Auto-detect your Python environment (uv → conda → venv)
2. Clone the repository to ~/mcp-creator-growth
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/mcp_creator_growth.git
cd mcp_creator_growth

# 2. Create virtual environment and install
# Using uv (recommended)
uv venv --python 3.11 mcp-creator-growth
source mcp-creator-growth/bin/activate          # macOS/Linux
# mcp-creator-growth\Scripts\activate           # Windows
uv pip install -e '.[dev]'

# Or using standard venv
python -m venv mcp-creator-growth
source mcp-creator-growth/bin/activate           # macOS/Linux
# mcp-creator-growth\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/mcp-creator-growth:latest

# Or build locally
git clone https://github.com/SunflowersLwtech/mcp_creator_growth.git
cd mcp_creator_growth
docker build -t mcp-creator-growth .

# 2. Run with Docker
docker run -i mcp-creator-growth

# 3. Or use Docker Compose
docker-compose up -d

For detailed Docker usage, persistent storage, and Claude Desktop integration, see DOCKER.md.

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⚙️ 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 mcp-creator-growth -- ~/mcp-creator-growth/mcp-creator-growth/bin/mcp-creator-growth

# Windows
claude mcp add mcp-creator-growth -- %USERPROFILE%\mcp-creator-growth\mcp-creator-growth\Scripts\mcp-creator-growth.exe

Option 2: Config File

Add to ~/.claude.json:

{
  "mcpServers": {
    "mcp-creator-growth": {
      "command": "~/mcp-creator-growth/mcp-creator-growth/bin/mcp-creator-growth"
    }
  }
}

For Windows:

{
  "mcpServers": {
    "mcp-creator-growth": {
      "command": "C:\\Users\\YourName\\mcp-creator-growth\\mcp-creator-growth\\Scripts\\mcp-creator-growth.exe"
    }
  }
}

Example paths:

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

Path breakdown (Unix example):

• ~/mcp-creator-growth → repository directory
• mcp-creator-growth → virtual environment directory created by uv/venv
• bin/mcp-creator-growth → executable

Cursor

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

{
  "mcp-creator-growth": {
    "command": "~/mcp-creator-growth/mcp-creator-growth/bin/mcp-creator-growth"
  }
}

For Windows:

{
  "mcp-creator-growth": {
    "command": "C:\\Users\\YourName\\mcp-creator-growth\\mcp-creator-growth\\Scripts\\mcp-creator-growth.exe"
  }
}

Windsurf

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

{
  "mcpServers": {
    "mcp-creator-growth": {
      "command": "~/mcp-creator-growth/mcp-creator-growth/bin/mcp-creator-growth"
    }
  }
}

Docker Configuration

To use Docker with any MCP-compatible IDE:

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

See 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: /mcp-creator-growth/bin/mcp-creator-growth (or mcp-creator-growth\Scripts\mcp-creator-growth.exe on Windows)
• Transport: stdio

After configuration, restart your IDE.

Usage

Available Tools

Tool	Trigger	For	Returns
learning_session	User explicit request	User	{status, action} - minimal
debug_search	Automatic (on error)	Agent	Compact summaries
debug_record	Automatic (after fix)	Agent	{ok, id} - minimal

For Users: Learning Session

Say to your AI assistant:

• "Quiz me on this change"
• "Test my understanding"
• "Help me learn about what you did"

The agent will create an interactive learning card and wait until you complete it.

Note: Quiz scores are saved locally for your self-tracking but are NOT returned to the agent - this keeps the context clean.

For Agents: Debug Tools

The debug tools work silently in the background:

• Search first: When encountering errors, agent searches past solutions
• Record after: When fixing errors, agent records the solution
• Progressive disclosure: Returns compact summaries, not full records
• Fast lookups: Uses inverted index for keyword-based searches

Updating

One-Line Update (Recommended)

The remote update script automatically detects your installation and works with any path format (including Chinese/non-ASCII paths):

macOS / Linux

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

Windows (PowerShell)

irm https://raw.githubusercontent.com/SunflowersLwtech/mcp_creator_growth/main/scripts/update.ps1 | iex

The update script will:

1. Auto-detect your installation location (supports multiple installations)
2. Pull the latest changes from the repository
3. Force-reinstall dependencies to ensure version synchronization
4. Verify installation integrity and report any issues
5. Detect if MCP server is in use and provide clear instructions

Why remote update?

• ✅ Works with Chinese/non-ASCII paths without cd navigation
• ✅ Always uses the latest update logic from the repository
• ✅ Auto-detects installation location even if you forgot where it is
• ✅ Handles multiple installations gracefully

Local Update (Alternative)

macOS / Linux:

~/mcp-creator-growth/scripts/update.sh

Windows (PowerShell):

~\mcp-creator-growth\scripts\update.ps1

Manual Update

Click to expand manual update steps

# Navigate to installation directory
cd ~/mcp-creator-growth  # or your custom installation path

# Pull latest changes
git pull origin main

# Update dependencies
# Using uv
source mcp-creator-growth/bin/activate          # macOS/Linux
# mcp-creator-growth\Scripts\activate           # Windows
uv pip install -e '.[dev]' --upgrade

# Or using standard venv
source mcp-creator-growth/bin/activate           # macOS/Linux
# mcp-creator-growth\Scripts\activate            # Windows
pip install -e '.[dev]' --upgrade

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🖼️ Screenshots

Learning Session WebUI

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🔒 Security & Privacy

Aspect	Details
Local First	All data stored in .mcp-sidecar/ directory within your project
No Telemetry	Zero data sent to external servers
Full Control	Delete .mcp-sidecar/ anytime to reset all data

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🔮 Roadmap

We're building toward a Personalized Learning Center that grows with you. Here's what's coming:

🔍 Advanced Search & Indexing (v1.2)

Feature	Description
SQLite FTS5	Full-text search with Chinese support, prefix matching, and boolean queries
BM25 Ranking	Industry-standard relevance scoring for better search results
Semantic Search	Vector embeddings for meaning-based matching (e.g., "权限错误" finds "permission denied")
Cross-project Search	Search debug experiences across all your projects

📱 Mobile App (v2.0)

Feature	Description
Learning History Sync	Access your quiz history and learning progress on mobile
Spaced Repetition	Smart review scheduling based on forgetting curves
Offline Mode	Learn anywhere, sync when connected
Push Notifications	Gentle reminders to review concepts you're forgetting

🎯 Personalized Learning Center (v2.5)

Feature	Description
Knowledge Graph	Visual map of concepts you've learned and their connections
Weakness Analysis	AI identifies areas where you struggle and suggests focused practice
Learning Streaks	Gamification to keep you motivated
Team Insights	(Optional) Share anonymized learning patterns with your team

🤖 AI Enhancements (v3.0)

Feature	Description
Adaptive Quizzes	Questions adjust difficulty based on your performance
Code Pattern Recognition	Learn from patterns in your own codebase
Multi-language Support	Explanations in your preferred language
Voice Interface	"Hey Claude, quiz me on what we did yesterday"

Want to influence the roadmap? Open an issue or join the discussion!

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🔧 Environment Variables

Variable	Default	Description
MCP_DEBUG	false	Enable debug logging (true, 1, yes, on)
MCP_TIMEOUT	120000	MCP server startup timeout in ms
MAX_MCP_OUTPUT_TOKENS	25000	Maximum tokens for MCP output

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🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Install dev dependencies: uv pip install -e '.[dev]'
4. Make changes and run tests: pytest
5. Submit a Pull Request

See CONTRIBUTING.md for detailed guidelines.

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📬 Contact

Channel	Address
Email	sunflowers0607@outlook.com
Email	weiliu0607@gmail.com
GitHub Issues	Open an Issue

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📄 License

This project is licensed under the MIT License.

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Built with FastMCP • MCP Standard • Glama MCP