GenZ

GenZ MCP Server delivers two essential AI-powered development tools in a slim, fast MCP server you can run locally or integrate into your development environment. It focuses on interactive chat for collaborative thinking and systematic debugging guidance, with support for multiple model providers and conversation context preservation to streamline your AI-assisted workflow.

How to use

You run GenZ MCP Server and connect with an MCP client to access GenZ Chat and GenZ Debug. Start a local MCP instance and configure your client to point at the local server, then switch between chat and debugging workflows as you analyze code, discuss approaches, and investigate root causes. When you start a session, your conversation context is preserved across interactions, and you can reference files and images during discussions. You can choose different AI models for diverse perspectives and enable web search for up-to-date information.

Practical usage patterns include starting a development chat to brainstorm architecture, performing a structured debugging session to trace a bug, and using file context during code reviews. For best results, provide clear context like file paths, relevant code snippets, and a description of the observed behavior. You can run multiple sessions and switch between GenZ Chat for design discussions and GenZ Debug for methodical investigation steps.

How to install

Prerequisites: Python 3.9 or newer. An API key for at least one supported provider (Gemini, OpenAI, OpenRouter, or a local Ollama setup). Ensure you have Python and the needed dependencies available on your system.

# 1) Clone the repository
# Replace with your actual fork URL if needed
git clone https://github.com/your-repo/genz-mcp-server
cd genz-mcp-server

# 2) Set up your Python environment
python -m venv .genz_venv
source .genz_venv/bin/activate  # On Windows use: .genz_venv\Scripts\activate

# 3) Install dependencies
pip install -r requirements.txt

# 4) Configure API keys (example keys shown; replace with your real keys)
# Copy example env file and edit
cp .env.example .env
# Edit .env with your API keys

5. Start the MCP server using the explicit command shown for local use. The server runs as a stdio process that you can connect to from your MCP client.

Additional content

Configuration and runtime notes: The server supports multiple model providers and preserves conversation context. To customize, adjust environment variables for API keys and provider URLs. If you plan to switch providers or run multiple instances, you can reuse the same local server configuration and point different MCP clients at separate process instances.

Security considerations: Protect API keys and restrict access to the local MCP endpoint. Use secure channels for any remote connections and rotate keys periodically. Ensure your environment variables are not exposed in shared logs or screenshots.

Troubleshooting tips: If the server fails to start, verify that Python 3.9+ is active, dependencies are installed, and environment variables are correctly set. Check the console output for missing key errors and ensure the path to server.py or equivalent entry point is correct.

Notes and examples

The focused server provides GenZ Chat for interactive development discussions and GenZ Debug for structured debugging work. You can run both tools in separate sessions or combine them within a single workflow to iterate quickly from design to implementation to verification.

Available tools

GenZ Chat

Interactive development chat and collaborative thinking with file context and multi-model support for ideas, explanations, and explanations of alternatives.

GenZ Debug

Systematic root cause analysis and debugging assistance with step-by-step investigation workflows, evidence tracking, and context-aware analysis.