MCP Creator Growth

MCP Creator Growth

An interactive learning assistant that helps developers understand AI-generated code changes through quizzes and blocking learning sessions. It tracks and searches debugging experiences using RAG to ensure users build long-term technical understanding rather than just copy-pasting solutions.

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<img src="assets/icon.png" width="48" height="48" align="top" style="margin-right: 10px;"> 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.

License: MIT Python 3.11+ MCP Standard Glama MCP DeepWiki


🌐 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

🚀 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.

📦 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.
get_system_info ℹ️ Utility Returns system environment information (platform, Python version, etc.).

Tool Details

<details> <summary><b>🎓 learning_session</b> - Interactive Learning Card</summary>

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"}

</details>

<details> <summary><b>🔍 debug_search</b> - Search Debug History</summary>

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}

</details>

<details> <summary><b>📝 debug_record</b> - Record Debug Experience</summary>

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": "..."}

</details>

<details> <summary><b>📚 term_get</b> - Get Programming Terms</summary>

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}

</details>


🛠️ Installation

One-Line Install (Recommended)

<table> <tr> <th>Platform</th> <th>Command</th> </tr> <tr> <td><b>macOS / Linux</b></td> <td>

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

</td> </tr> <tr> <td><b>Windows (PowerShell)</b></td> <td>

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

</td> </tr> </table>

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

<details> <summary>Click to expand manual installation steps</summary>

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 .venv
source .venv/bin/activate          # macOS/Linux
# .venv\Scripts\activate           # Windows
uv pip install -e ".[dev]"

# Or using standard venv
python -m venv venv
source venv/bin/activate           # macOS/Linux
# venv\Scripts\activate            # Windows
pip install -e ".[dev]"

</details>


⚙️ IDE Configuration

Claude Code (CLI) — One Command Setup

After installation, configure Claude Code with a single command:

<table> <tr> <th>Platform</th> <th>Command</th> </tr> <tr> <td><b>macOS / Linux</b></td> <td>

# User scope (available across all projects)
claude mcp add --scope user mcp-creator-growth -- ~/mcp-creator-growth/.venv/bin/mcp-creator-growth

# Or project scope (shared with team via .mcp.json)
claude mcp add --scope project mcp-creator-growth -- ~/mcp-creator-growth/.venv/bin/mcp-creator-growth

</td> </tr> <tr> <td><b>Windows (PowerShell)</b></td> <td>

# User scope
claude mcp add --scope user mcp-creator-growth -- "$env:USERPROFILE\mcp-creator-growth\.venv\Scripts\mcp-creator-growth.exe"

# Or project scope
claude mcp add --scope project mcp-creator-growth -- "$env:USERPROFILE\mcp-creator-growth\.venv\Scripts\mcp-creator-growth.exe"

</td> </tr> </table>

Verify installation:

claude mcp list                    # List all MCP servers
claude mcp get mcp-creator-growth  # Check this server's status

Manual JSON Configuration

For Claude Desktop, Cursor, Windsurf, or other MCP-compatible IDEs:

<table> <tr> <th>IDE</th> <th>Config File Location</th> </tr> <tr> <td>Claude Desktop</td> <td>

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json
  • Linux: ~/.config/Claude/claude_desktop_config.json

</td> </tr> <tr> <td>Claude Code (User)</td> <td><code>~/.claude.json</code></td> </tr> <tr> <td>Claude Code (Project)</td> <td><code>.mcp.json</code> in project root</td> </tr> <tr> <td>Cursor</td> <td>Settings → MCP → Add New MCP Server</td> </tr> <tr> <td>Windsurf</td> <td><code>~/.codeium/windsurf/mcp_config.json</code></td> </tr> </table>

JSON Configuration:

<details> <summary><b>macOS / Linux</b></summary>

{
  "mcpServers": {
    "mcp-creator-growth": {
      "command": "/Users/YOUR_USERNAME/mcp-creator-growth/.venv/bin/mcp-creator-growth",
      "args": []
    }
  }
}

</details>

<details> <summary><b>Windows</b></summary>

{
  "mcpServers": {
    "mcp-creator-growth": {
      "command": "C:\\Users\\YOUR_USERNAME\\mcp-creator-growth\\.venv\\Scripts\\mcp-creator-growth.exe",
      "args": []
    }
  }
}

</details>

Note: Replace YOUR_USERNAME with your actual username, or use the full path printed by the installer.


🖼️ Screenshots

Learning Session WebUI

WebUI Preview


🔒 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

🔧 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

🤝 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.


📬 Contact

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

📄 License

This project is licensed under the MIT License.


<p align="center"> Built with <a href="https://github.com/jlowin/fastmcp">FastMCP</a> • <a href="https://modelcontextprotocol.io">MCP Standard</a> • <a href="https://glama.ai/mcp/servers/@SunflowersLwtech/mcp_creator_growth">Glama MCP</a> </p>

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