The Model Context Protocol (MCP) is an open standard for connecting AI agents to data sources and tools. It enables secure, controlled access to local and remote resources through a standardized protocol.

Examples

Sample	File	Description
Agent as MCP Server	`agent_as_mcp_server.py`	Shows how to expose an Agent Framework agent as an MCP server that other AI applications can connect to
API Key Authentication	`mcp_api_key_auth.py`	Demonstrates API key authentication with MCP servers using `header_provider`, runtime invocation kwargs, and a command-line API key argument
GitHub Integration with PAT	`mcp_github_pat.py`	Demonstrates connecting to GitHub's MCP server using Personal Access Token (PAT) authentication
Long-Running Task	`mcp_long_running_task.py`	Demonstrates transparent SEP-2663 long-running task handling for MCP tools that advertise `taskSupport=required`. Self-spawns a stdio MCP child server

Prerequisites

Most samples in this folder use OpenAI:

OPENAI_API_KEY environment variable
OPENAI_CHAT_MODEL environment variable

Run mcp_api_key_auth.py with the MCP API key as the first command-line argument.

For mcp_github_pat.py:

GITHUB_PAT - Your GitHub Personal Access Token (create at https://github.com/settings/tokens)

For mcp_long_running_task.py (uses Azure OpenAI via Entra-ID):

Run az login once
AZURE_OPENAI_ENDPOINT - your Azure OpenAI resource endpoint, e.g. https://<resource>.openai.azure.com/
AZURE_OPENAI_CHAT_MODEL (or AZURE_OPENAI_MODEL) - the deployment name (e.g. gpt-4o-mini)