MCP (Model Context Protocol) Examples

This folder contains examples demonstrating how to work with MCP using Agent Framework.

What is MCP?

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
Progressive Disclosure	mcp_progressive_disclosure.py	Demonstrates use_progressive_disclosure, always_load, allowed_tools, and prefixed list_mcp_tools / load_tool / unload_tool names. load_tool and unload_tool can accept one tool name or multiple names. Self-spawns a stdio MCP child server
Sampling Approval	mcp_sampling_approval.py	Demonstrates gating server-initiated sampling/createMessage requests with a sampling_approval_callback, plus the sampling_max_tokens and sampling_max_requests guardrails. MCP sampling is denied by default

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.

mcp_progressive_disclosure.py self-spawns its demo MCP stdio server; no separate MCP server setup is required.

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)