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feat: add LangChain & Agent Framework MCP examples, update prompts, deps, and docs
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‎.env-sample‎

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# API Host Selection
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# Options: github, azure, ollama, openai
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API_HOST=github
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# GitHub Models Configuration
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GITHUB_TOKEN=your_github_token_here
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GITHUB_MODEL=gpt-4o
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# Azure OpenAI Configuration
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AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
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AZURE_OPENAI_CHAT_DEPLOYMENT=your-deployment-name
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AZURE_OPENAI_VERSION=2024-02-15-preview
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# Ollama Configuration
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OLLAMA_MODEL=llama3.1
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OLLAMA_ENDPOINT=http://localhost:11434/v1
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OLLAMA_API_KEY=ollama
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# OpenAI Configuration (default if API_HOST not set)
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OPENAI_MODEL=gpt-4o-mini
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OPENAI_API_KEY=your_openai_api_key_here

‎.vscode/launch.json‎

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"version": "0.2.0",
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"configurations": [
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{
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"name": "Attach to MCP Server",
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"name": "Launch MCP HTTP Server (Debug)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/basic_mcp_http.py",
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"console": "integratedTerminal",
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"cwd": "${workspaceFolder}",
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"env": {
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"PYTHONUNBUFFERED": "1"
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}
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},
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{
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"name": "Launch MCP stdio Server (Debug)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/basic_mcp_stdio.py",
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"console": "integratedTerminal",
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"cwd": "${workspaceFolder}",
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"env": {
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"PYTHONUNBUFFERED": "1"
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}
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},
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{
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"name": "Attach to MCP Server (stdio)",
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"type": "debugpy",
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"request": "attach",
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"connect": {
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"remoteRoot": "${workspaceFolder}"
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}
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]
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},
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{
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"name": "Attach to MCP Server (HTTP)",
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"type": "debugpy",
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"request": "attach",
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"connect": {
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"host": "localhost",
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"port": 5679
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},
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"pathMappings": [
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{
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"localRoot": "${workspaceFolder}",
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"remoteRoot": "${workspaceFolder}"
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}
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]
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}
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]
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}

‎.vscode/mcp.json‎

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"expenses-mcp-http": {
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"type": "http",
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"url": "http://localhost:8000/mcp"
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},
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"expenses-mcp-http-debug": {
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"type": "stdio",
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"command": "uv",
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"cwd": "${workspaceFolder}",
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"args": [
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"run",
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"--",
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"python",
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"-m",
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"debugpy",
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"--listen",
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"0.0.0.0:5679",
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"--wait-for-client",
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"basic_mcp_http.py"
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],
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"env": {
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"PYTHONUNBUFFERED": "1"
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}
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}
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},
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"inputs": []

‎README.md‎

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# Python MCP Demo
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A demonstration project showcasing Model Context Protocol (MCP) implementations using FastMCP, with examples of stdio, HTTP transports, and integration with LangChain and Agent Framework.
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## Table of Contents
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- [Prerequisites](#prerequisites)
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- [Setup](#setup)
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- [Python Scripts](#python-scripts)
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- [MCP Server Configuration](#mcp-server-configuration)
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- [Debugging](#debugging)
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- [License](#license)
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## Prerequisites
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- Python 3.13 or higher
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- [uv](https://docs.astral.sh/uv/)
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- API access to one of the following:
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- GitHub Models (GitHub token)
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- Azure OpenAI (Azure credentials)
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- Ollama (local installation)
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- OpenAI API (API key)
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## Setup
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1. Install dependencies using `uv`:
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```bash
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uv sync
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```
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2. Copy `.env-sample` to `.env` and configure your environment variables:
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```bash
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cp .env-sample .env
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```
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3. Edit `.env` with your API credentials. Choose one of the following providers by setting `API_HOST`:
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- `github` - GitHub Models (requires `GITHUB_TOKEN`)
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- `azure` - Azure OpenAI (requires Azure credentials)
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- `ollama` - Local Ollama instance
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- `openai` - OpenAI API (requires `OPENAI_API_KEY`)
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## Python Scripts
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Run any script with: `uv run <script_name>`
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- **basic_mcp_http.py** - MCP server with HTTP transport on port 8000
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- **basic_mcp_stdio.py** - MCP server with stdio transport for VS Code integration
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- **langchainv1_mcp_http.py** - LangChain agent with MCP tool integration and temporal context handling
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- **agentframework_mcp_learn.py** - Microsoft Agent Framework integration with MCP
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## MCP Server Configuration
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### Using with MCP Inspector
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The [MCP Inspector](https://github.com/modelcontextprotocol/inspector) is a developer tool for testing and debugging MCP servers.
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> **Note:** While HTTP servers can technically work with port forwarding in Codespaces/Dev Containers, the setup for MCP Inspector and debugger attachment is not straightforward. For the best development experience with full debugging capabilities, we recommend running this project locally.
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**For stdio servers:**
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```bash
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npx @modelcontextprotocol/inspector uv run basic_mcp_stdio.py
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```
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**For HTTP servers:**
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1. Start the HTTP server:
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```bash
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uv run basic_mcp_http.py
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```
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2. In another terminal, run the inspector:
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```bash
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npx @modelcontextprotocol/inspector http://localhost:8000/mcp
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```
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The inspector provides a web interface to:
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- View available tools, resources, and prompts
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- Test tool invocations with custom parameters
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- Inspect server responses and errors
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- Debug server communication
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### Using with GitHub Copilot
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The `.vscode/mcp.json` file configures MCP servers for GitHub Copilot integration:
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**Available Servers:**
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- **expenses-mcp**: stdio transport server for production use
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- **expenses-mcp-debug**: stdio server with debugpy on port 5678
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- **expenses-mcp-http**: HTTP transport server at `http://localhost:8000/mcp`
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- **expenses-mcp-http-debug**: stdio server with debugpy on port 5679
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**Switching Servers:**
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Configure which server GitHub Copilot uses by selecting it in the Chat panel selecting the tools icon.
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## Debugging
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### Debug Configurations
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The `.vscode/launch.json` provides four debug configurations:
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#### Launch Configurations (Start server with debugging)
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1. **Launch MCP HTTP Server (Debug)**
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- Directly starts `basic_mcp_http.py` with debugger attached
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- Best for: Standalone testing and LangChain script debugging
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2. **Launch MCP stdio Server (Debug)**
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- Directly starts `basic_mcp_stdio.py` with debugger attached
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- Best for: Testing stdio communication
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#### Attach Configurations (Attach to running server)
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3. **Attach to MCP Server (stdio)** - Port 5678
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- Attaches to server started via `expenses-mcp-debug` in `mcp.json`
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- Best for: Debugging during GitHub Copilot Chat usage
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4. **Attach to MCP Server (HTTP)** - Port 5679
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- Attaches to server started via `expenses-mcp-http-debug` in `mcp.json`
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- Best for: Debugging HTTP server during Copilot usage
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### Debugging Workflow
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#### Option 1: Launch and Debug (Standalone)
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Use this approach for debugging with MCP Inspector or LangChain scripts:
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1. Set breakpoints in `basic_mcp_http.py` or `basic_mcp_stdio.py`
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2. Press `Cmd+Shift+D` to open Run and Debug
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3. Select "Launch MCP HTTP Server (Debug)" or "Launch MCP stdio Server (Debug)"
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4. Press `F5` or click the green play button
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5. Connect MCP Inspector or run your LangChain script to trigger breakpoints
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- For HTTP: `npx @modelcontextprotocol/inspector http://localhost:8000/mcp`
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- For stdio: `npx @modelcontextprotocol/inspector uv run basic_mcp_stdio.py` (start without debugger first)
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#### Option 2: Attach to Running Server (Copilot Integration)
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1. Set breakpoints in your MCP server file
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1. Start the debug server via `mcp.json` configuration:
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- Select `expenses-mcp-debug` or `expenses-mcp-http-debug`
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1. Press `Cmd+Shift+D` to open Run and Debug
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1. Select appropriate "Attach to MCP Server" configuration
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1. Press `F5` to attach
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1. Select correct expense mcp server in GitHub Copilot Chat tools
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1. Use GitHub Copilot Chat to trigger the MCP tools
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1. Debugger pauses at breakpoints
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## License
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MIT

‎agentframework_mcp_learn.py‎

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from __future__ import annotations
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import asyncio
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import logging
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import os
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from dotenv import load_dotenv
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from rich import print
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from rich.logging import RichHandler
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from agent_framework import ChatAgent, MCPStreamableHTTPTool
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from agent_framework.azure import AzureOpenAIChatClient
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from agent_framework.openai import OpenAIChatClient
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from azure.identity import DefaultAzureCredential
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logging.basicConfig(level=logging.WARNING, format="%(message)s", datefmt="[%X]", handlers=[RichHandler()])
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logger = logging.getLogger("learn_mcp_lang")
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load_dotenv(override=True)
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API_HOST = os.getenv("API_HOST", "github")
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if API_HOST == "azure":
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client = AzureOpenAIChatClient(
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credential=DefaultAzureCredential(),
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deployment_name=os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT"),
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endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
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api_version=os.environ.get("AZURE_OPENAI_VERSION"),
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)
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elif API_HOST == "github":
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client = OpenAIChatClient(
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base_url="https://models.github.ai/inference",
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api_key=os.environ["GITHUB_TOKEN"],
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model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4o"),
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)
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elif API_HOST == "ollama":
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client = OpenAIChatClient(
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base_url=os.environ.get("OLLAMA_ENDPOINT", "http://localhost:11434/v1"),
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api_key="none",
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model_id=os.environ.get("OLLAMA_MODEL", "llama3.1:latest"),
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)
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else:
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client = OpenAIChatClient(
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api_key=os.environ.get("OPENAI_API_KEY"), model_id=os.environ.get("OPENAI_MODEL", "gpt-4o")
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)
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async def http_mcp_example():
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async with (
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MCPStreamableHTTPTool(
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name="Microsoft Learn MCP",
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url="https://learn.microsoft.com/api/mcp",
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headers={"Authorization": "Bearer your-token"},
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) as mcp_server,
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ChatAgent(
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chat_client=client,
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name="DocsAgent",
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instructions="You help with Microsoft documentation questions.",
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) as agent,
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):
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result = await agent.run(
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"How to create an Azure storage account using az cli?",
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tools=mcp_server
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)
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print(result)
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if __name__ == "__main__":
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asyncio.run(http_mcp_example())

‎basic_mcp_http.py‎

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@mcp.prompt
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def create_expense_prompt(
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date: str,
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amount: float,
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category: str,
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description: str,
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payment_method: str
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def analyze_spending_prompt(
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category: str | None = None,
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start_date: str | None = None,
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end_date: str | None = None
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) -> str:
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"""Generate a prompt to analyze spending patterns with optional filters."""
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"""Generate a prompt to add a new expense using the add_expense tool."""
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logger.info(f"Expense prompt created for: {description}")
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return f"""
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Please add the following expense:
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- Date: {date}
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- Amount: ${amount}
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- Category: {category}
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- Description: {description}
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- Payment Method: {payment_method}
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Use the `add_expense` tool to record this transaction.
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"""
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filters = []
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if category:
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filters.append(f"Category: {category}")
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if start_date:
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filters.append(f"From: {start_date}")
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if end_date:
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filters.append(f"To: {end_date}")
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filter_text = f" ({', '.join(filters)})" if filters else ""
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return f"""
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Please analyze my spending patterns{filter_text} and provide:
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1. Total spending breakdown by category
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2. Average daily/weekly spending
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3. Most expensive single transaction
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4. Payment method distribution
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5. Spending trends or unusual patterns
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6. Recommendations for budget optimization
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Use the expense data to generate actionable insights.
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"""
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if __name__ == "__main__":
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logger.info("MCP Expenses server starting (HTTP mode on port 8000)")
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# Run in HTTP mode on port 8000
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mcp.run(transport="http", host="0.0.0.0", port=8000)

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