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Eduard van Valkenburg
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* Add sampling guardrails to MCP tools Add approval, token, and request-count controls to the MCP sampling callback used when an MCPTool is configured with a chat client. - Add `sampling_approval_callback`, `sampling_max_tokens`, and `sampling_max_requests` parameters to `MCPTool` and its `MCPStdioTool`, `MCPStreamableHTTPTool`, and `MCPWebsocketTool` subclasses, positioned directly after `client`. - Gate each server-initiated `sampling/createMessage` request behind the approval callback, which denies by default when no callback is provided. - Clamp the requested `maxTokens` to `sampling_max_tokens` and enforce a per-session request count via `sampling_max_requests`. - Log incoming sampling requests at WARNING level (counts only). - Export `SamplingApprovalCallback` from the public API. - Add tests, a sample, and documentation updates. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Make sampling denial message context-aware Distinguish the deny-by-default case (no approval callback configured) from an explicit denial by a configured `sampling_approval_callback`, so the returned ErrorData message is accurate for callback-driven denials and exceptions. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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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 |
| 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_KEYenvironment variableOPENAI_CHAT_MODELenvironment 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 loginonce AZURE_OPENAI_ENDPOINT- your Azure OpenAI resource endpoint, e.g.https://<resource>.openai.azure.com/AZURE_OPENAI_CHAT_MODEL(orAZURE_OPENAI_MODEL) - the deployment name (e.g.gpt-4o-mini)