MCP Lab

A development environment for building and testing custom MCP servers with AI and VS Code integration.

GitHub

Lab for developing custom MCP (Multi-Component Protocol) servers integrated with AI tooling. Enables modular AI workflows via VS Code-compatible servers. Designed for one-person developers building structured agent pipelines. Supports prompt design, sampling control, and tool orchestration. Includes example agents and tools for rapid iteration. Great for creating and debugging advanced agent infrastructure.


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Table of Contents

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
  1. Install dependencies:
pip install -r requirements.txt
  1. 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
  1. Test server functionality:
python tests/test_server.py
  1. 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

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