Serves as the API framework for the MCP provider, allowing agents to access context and actions through a REST API at http://localhost:8000.
Enables testing of MCP functionality through test scripts, helping users verify the proper operation of context access and actions.
Powers the implementation of both the MCP provider and agent, allowing users to extend the provider and agent functionality through Python code.
MCP Hands-On Learning & Demo Guide
This project teaches the core concepts of the Model Context Protocol (MCP) through hands-on Python code.
What is MCP?
MCP (Model Context Protocol) is an open protocol for standardizing how AI models, tools, and agents interact with their context (files, code, resources, etc.).
Project Structure
models.py
: MCP data modelsprovider.py
: FastAPI MCP provideragent.py
: MCP agent scripttest_mcp.py
: Example testsrequirements.txt
: Python dependencies
Quickstart for New Users
1. Clone or Download This Repository
2. Set Up Python Environment
Open a terminal in the mcp
directory and run:
3. Start the MCP Provider
In the same terminal, run:
This starts the FastAPI MCP provider at http://localhost:8000.
4. Run the MCP Agent
Open a new terminal, activate the environment, and run:
You should see provider info, context items, and a read action result.
5. Run the Tests (Optional)
How It Works
- The provider exposes context and actions via a REST API.
- The agent interacts with the provider to perform actions.
- You can extend the provider and agent to add more actions or context types.
For questions or to extend this demo, edit the Python files as needed!
This server cannot be installed
A hands-on demonstration project that teaches the Model Context Protocol (MCP) through Python code, allowing users to understand how AI models interact with their context through a provider-agent architecture.
Related MCP Servers
- -securityFlicense-qualityFacilitates interaction and context sharing between AI models using the standardized Model Context Protocol (MCP) with features like interoperability, scalability, security, and flexibility across diverse AI systems.Last updated -1Python
- -securityAlicense-qualityA streamlined foundation for building Model Context Protocol servers in Python, designed to make AI-assisted development of MCP tools easier and more efficient.Last updated -14PythonMIT License
- -security-license-qualityAn open-source implementation of the Model Context Protocol (MCP) that bridges AI agents with enterprise systems, enabling secure access to real-world data and capabilities.Last updated -1PythonApache 2.0
- -securityFlicense-qualityA Python implementation of the Model Context Protocol (MCP) that connects client applications with AI models, primarily Anthropic's models, with setup instructions for local development and deployment.Last updated -Python