mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Eduard van Valkenburg
9a56bc9f16
* 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>
35 lines
2.1 KiB
Markdown
35 lines
2.1 KiB
Markdown
# MCP (Model Context Protocol) Examples
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This folder contains examples demonstrating how to work with MCP using Agent Framework.
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## What is MCP?
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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.
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## Examples
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| Sample | File | Description |
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| **Agent as MCP Server** | [`agent_as_mcp_server.py`](agent_as_mcp_server.py) | Shows how to expose an Agent Framework agent as an MCP server that other AI applications can connect to |
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| **API Key Authentication** | [`mcp_api_key_auth.py`](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 |
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| **GitHub Integration with PAT** | [`mcp_github_pat.py`](mcp_github_pat.py) | Demonstrates connecting to GitHub's MCP server using Personal Access Token (PAT) authentication |
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| **Long-Running Task** | [`mcp_long_running_task.py`](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 |
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| **Sampling Approval** | [`mcp_sampling_approval.py`](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 |
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## Prerequisites
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Most samples in this folder use OpenAI:
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- `OPENAI_API_KEY` environment variable
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- `OPENAI_CHAT_MODEL` environment variable
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Run `mcp_api_key_auth.py` with the MCP API key as the first command-line argument.
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For `mcp_github_pat.py`:
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- `GITHUB_PAT` - Your GitHub Personal Access Token (create at https://github.com/settings/tokens)
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For `mcp_long_running_task.py` (uses Azure OpenAI via Entra-ID):
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- Run `az login` once
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- `AZURE_OPENAI_ENDPOINT` - your Azure OpenAI resource endpoint, e.g. `https://<resource>.openai.azure.com/`
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- `AZURE_OPENAI_CHAT_MODEL` (or `AZURE_OPENAI_MODEL`) - the deployment name (e.g. `gpt-4o-mini`)
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