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.

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🚀 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.

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📚 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

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🌐 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

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🔧 Requirements

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

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🏃‍♂️ Quick Start

```bash