MCP AI Agents LAB 🤖📚

Model Context Protocol (MCP) + AI Agents: A suite of advanced projects that explore, implement, and document AI agent architectures powered by standardized context protocols.

This repository serves as a unified hub for cutting-edge MCP-based agent systems, with full documentation, protocol guides, and open-source tools.

🚀 Projects in this Suite

• 🧠 MCP Agent Framework: Build modular, interoperable AI agents that communicate via Model Context Protocol.
• 🔄 MCP Message Handler: Universal handler for context injection and protocol message formatting.
• 📦 Dataset Tools: Tools to convert real-world context data into MCP-compliant datasets.
• 📝 Context Chain Builder: Automate the chaining of multiple MCP messages to simulate complex tasks.
• 🌐 MCP Proxy Layer: Middleware to connect MCP agents with APIs, databases, and models (LLMs, RAG systems).
• 🤖 Example Agents: Reference AI agents (task executors, summarizers, planners) built fully on MCP.

📚 Documentation

Explore full guides and technical breakdowns:

• 🌐 What is Model Context Protocol?
• 🛠️ Building an MCP Agent
• 📦 MCP Message Format Spec
• 🔗 Chaining MCP Contexts
• 🧑‍💻 Running Example Agents

📖 Start here: Getting Started Guide

🌐 Useful External Links

• 📄 MCP Official Spec: https://modelcontext.org/spec
• 💬 MCP Community Forum: https://community.modelcontext.org
• 🔗 LangChain MCP Integration: https://github.com/langchain-ai/langchain
• 🧩 OpenAI MCP Resources: https://platform.openai.com/docs

🔧 Requirements

• Python 3.10+
• pydantic, requests, fastapi (for protocol servers)
• Optional: torch, transformers (for LLM-backed agents)

🏃‍♂️ Quick Start

# Clone the repo
git clone https://github.com/yourusername/mcp_ai_lab.git
cd mcp_ai_lab

# Install requirements
pip install -r requirements.txt

# Run an example agent
python agents/example_agent.py