MCP Learning Demo
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.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Learning Demoshow me how to set up the MCP provider"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.).
Related MCP server: MCP Server Demo
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:
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt3. Start the MCP Provider
In the same terminal, run:
uvicorn provider:app --reloadThis starts the FastAPI MCP provider at http://localhost:8000.
4. Run the MCP Agent
Open a new terminal, activate the environment, and run:
source venv/bin/activate
python agent.pyYou should see provider info, context items, and a read action result.
5. Run the Tests (Optional)
pytest test_mcp.pyHow 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!
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