MCP Lab

---

Table of Contents

• MCP Lab
• Features
• Installation
• Quick Start
• Project Structure
• Example Servers
• Benefits
• How It Compares
• License

MCP Lab

A development environment for building and testing custom MCP (Multi-Component Protocol) servers that integrate seamlessly with AI tooling and VS Code-compatible environments.

Features

• Custom MCP server development framework
• VS Code and Claude Desktop integration
• Structured agent pipeline architecture
• Prompt design and sampling control
• Tool orchestration and debugging
• Example agents for rapid iteration
• Modular AI workflow components

Installation

1. Clone the repository:

git clone https://github.com/harehimself/mcp-lab.git
cd mcp-lab

2. Install dependencies:

pip install -r requirements.txt

3. Configure your environment:

cp .env.example .env
# Edit .env with your API keys and settings

Quick Start

1. Start a basic MCP server:

python src/servers/basic_server.py

2. Test server functionality:

python tests/test_server.py

3. Integrate with Claude Desktop by adding to your configuration:

{
  "mcpServers": {
    "mcp-lab": {
      "command": "python",
      "args": ["path/to/mcp-lab/src/servers/main_server.py"]
    }
  }
}

Project Structure

• src/servers/: MCP server implementations
• src/tools/: Custom tool definitions
• src/agents/: Example agent configurations
• src/utils/: Utility functions and helpers
• tests/: Testing framework and examples
• examples/: Sample workflows and integrations

Example Servers

The lab includes several pre-built servers:

• File Operations: File system interaction and management
• Database Tools: Database query and manipulation tools
• API Integration: External API connection handlers
• Data Processing: Text and data transformation utilities
• Code Analysis: Code parsing and analysis tools

Benefits

• Accelerates MCP server development with proven patterns
• Provides structured approach to agent pipeline creation
• Enables rapid prototyping and debugging of AI workflows
• Offers reusable components for common AI tasks
• Simplifies integration with existing development environments

How It Compares

• Purpose-built for solo developers creating agent infrastructure
• Focuses on modularity and rapid iteration over enterprise features
• Lightweight alternative to complex agent frameworks
• Seamless VS Code and Claude Desktop integration
• Emphasizes debugging and development experience

License

MIT License © 2025 HareLabs