MCP Learn - Model Context Protocol Examples

This repository contains examples of using the Model Context Protocol (MCP).

Overview

MCP allows Large Language Models (LLMs) to call external tools via a standardized protocol. This repository includes:

MCP clients

• A cmdline client that can connect to any MCP server (clients/cmd_client.py)
• A chainlit client that can connect to any MCP server (clients/chainlit_client.py)

Agents that use MCP

• A langgraph agent that uses MCP to call external tools (agents/langgraph_agent.py)
• A semantic kernel agent that uses MCP to call external tools (agents/sk_agent.py)
• An autogen agent that uses MCP to call external tools (agents/autogen_agent.py)
• An openai agent that uses MCP to call external tools (agents/openai_agent.py)
• An ADK(google) agent that uses MCP to call external tools (agents/adk_agent)
• A pydantic agent that uses MCP to call external tools (agents/pydantic_agent.py)
• An agno agent that uses MCP to call external tools (agents/agno_agent.py)
• A smolagents agent that uses MCP to call external tools (agents/smol_agent.py)
• An atomic agent that uses MCP to call external tools (agents/atomic_agent.py)

MCP servers

• A weather information server that provides weather forecasts and alerts (servers/weather_server.py)
• A stock information server that provides stock prices and company information (servers/stock_server.py)
• A NL2SQL server that translates natural language queries to SQL (servers/nl2sql_server.py)
• A time server that provides the current time (servers/timer_server.py)
• A search server that provides search capabilities (servers/search_server.py)
• A composite server that aggregrtes all MCP servers (servers/composite_server.py)

Setup

Prerequisites

1. Python 3.10 or higher
2. uv for Python package management

Installation

# Clone this repository
git clone https://github.com/huangyingting/mcp-learn.git
cd mcp-learn

# Install dependencies
uv sync

Environment Setup

Create a .env file in servers, clients, and agents directories by copying the .env.example file to .env:

cp .env.example .env

Update the .env file with your Azure OpenAI API key and other configurations as needed.

Running the Examples

Starting a Server

All MCP servers are located in the servers/ directory. For example, to start the weather or stock server (both support two transport methods):

1. stdio (default): For direct communication with the client
2. sse: For running as a web server

To start a server with SSE transport:

uv run servers/any_mcp_server.py -t sse

When using the SSE transport, the server starts on http://localhost:8000/sse by default.

There is a composite server that aggregates all MCP servers. To start the composite server:

uv run servers/composite_server.py -t sse

Connecting the Client

Client are located in the clients/ directory. The client can connect to either a Python script server (stdio) or an SSE server:

To connect to a stdio server:

uv run clients/client.py servers/any_mcp_server.py

To connect to an SSE server:

uv run clients/client.py http://localhost:8000/sse

Once connected, you can interact with the client by typing in queries. For example:

• Weather server: "What's the weather forecast for latitude 40.7, longitude -74.0?" or "Are there any weather alerts in CA?"
• Stock server: "What's the current price of AAPL?" or "Give me information about Google's stock"

Type quit or exit to end your session.

For the chainlit client, you can run it with the following command:

uv run chainlit run clients/chainlit_client.py --port 9000

Then, open your web browser and navigate to http://localhost:9000 to interact with the client.

Use the Agent

The agent is located in the agents/ directory. To run the agent, use the following command:

To run semantic kernel agent

uv run agents/sk_agent.py

To run langgraph agent

uv run agents/langgraph_agent.py

To run autogen agent

uv run agents/autogen_agent.py

To run openai agent

uv run agents/openai_agent.py

To run ADK agent

uv run adk web --port 9000

Then, open your web browser and navigate to http://localhost:9000 to interact with the agent through adk web.

To run pydantic agent

uv run agents/pydantic_agent.py

To run agno agent

uv run agents/agno_agent.py

To run smolagents agent

uv run agents/smol_agent.py

To run atomic agent

uv run agents/atomic_agent.py

Available MCP servers

Weather Server

• get_alerts: Get weather alerts for a US state using a two-letter state code
• get_forecast: Get a weather forecast for a location by latitude and longitude

Stock Server

• current_date: Get the current date
• stock_price: Get the stock price for a given ticker
• stock_info: Get information about a company by ticker
• income_statement: Get the quarterly income statement for a company

NL2SQL Server

• query_sql: Execute a custom SQL query on the SQLite database
• list_tables: List all tables in the SQLite database
• describe_table: Get the structure of a specific table
• execute_nonquery: Execute a non-query SQL statement (INSERT, UPDATE, DELETE, etc.)
• database_info: Get general information about the connected SQLite database

Time Server

• get_current_time: Get the current time in a specific timezone

Search Server

• web_search: Search the web for a given query

News Server

• news_top_headlines: Get top headlines from NewsAPI.
• news_search: Search for news articles using NewsAPI.
• news_sources: Get available news sources from