MCP AI Agents LAB logo

MCP AI Agents LAB

by techySPHINX

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.

View on GitHub

Last updated: N/A

What is MCP AI Agents LAB?

The MCP AI Agents LAB is a collection of projects focused on building AI agents that communicate using the Model Context Protocol (MCP). It provides tools, frameworks, and examples for creating modular and interoperable agents.

How to use MCP AI Agents LAB?

To use this repository, clone the repo, install the requirements, and explore the example agents. Refer to the documentation for guides on building MCP agents, formatting messages, and chaining contexts. Start with the 'Getting Started Guide' for a comprehensive introduction.

Key features of MCP AI Agents LAB

  • MCP Agent Framework

  • MCP Message Handler

  • Dataset Tools

  • Context Chain Builder

  • MCP Proxy Layer

  • Example Agents

Use cases of MCP AI Agents LAB

  • Building task executors

  • Creating summarization agents

  • Developing AI planners

  • Connecting agents with APIs

  • Integrating with LLMs and RAG systems

  • Converting real-world context data

FAQ from MCP AI Agents LAB

What is Model Context Protocol (MCP)?

MCP is a standardized protocol for AI agents to communicate and share context.

What are the key requirements to run the projects?

You need Python 3.10+ and dependencies like pydantic, requests, and fastapi. Optional dependencies include torch and transformers for LLM-backed agents.

Where can I find the official MCP specification?

The official MCP specification can be found at https://modelcontext.org/spec

How can I contribute to the project?

Contributions are welcome! Please refer to the contribution guidelines for more information.

Where can I ask questions and get support?

You can ask questions and get support on the MCP Community Forum at https://community.modelcontext.org