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agent-framework/python/samples/02-agents/skills/mcp_based_skill
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SergeyMenshykh f31ed0ded9 Use MCPStreamableHTTPTool in MCP skills sample
Replace raw mcp library imports (ClientSession, streamable_http_client)
with the framework's MCPStreamableHTTPTool to keep MCP server connections
consistent regardless of whether skills are enabled.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
f31ed0ded9 · 2026-06-02 11:29:28 +01:00
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MCP-Based Agent Skills Sample

This sample demonstrates how to discover Agent Skills served over MCP with an Agent.

What it demonstrates

  • Connecting to a remote MCP server (over streamable HTTP) that exposes skill resources following the SEP-2640 convention.
  • Building a SkillsProvider from an MCPSkillsSource, which reads skill://index.json (SEP-2640 canonical discovery) and constructs skills from the index entries.
  • The progressive disclosure pattern across MCP: advertise → load → read resources, exactly as for filesystem-backed skills.

Running the Sample

Prerequisites

  • Python 3.10+
  • An Azure AI Foundry project with a deployed model
  • Azure CLI authentication (az login)
  • A running MCP server that hosts SEP-2640 skill resources (see "Providing an MCP server" below)

Setup

Set the following environment variables (in a .env file or your shell):

$env:FOUNDRY_PROJECT_ENDPOINT="https://your-endpoint.services.ai.azure.com/api/projects/your-project"
$env:FOUNDRY_MODEL="gpt-4o-mini"
$env:MCP_SKILLS_SERVER_URL="https://your-mcp-server.example.com/mcp"

Run

python mcp_based_skill.py

Providing an MCP server

This sample is a consumer: it does not host an MCP server itself. To try it end-to-end you need an MCP server that exposes the SEP-2640 skill resources (skill://index.json plus per-skill SKILL.md).