Mcp

How an AI agent reaches Microsoft Graph as the signed-in user through MCP — why token passthrough fails, and how on-behalf-of, Conditional Access, and RBAC secure the chain.

One control plane for enterprise AI — how APIM’s AI gateway gives security, network, and infrastructure teams governance over models, MCP tools, and agents. The full v2 surface.

Before MCP existed, adding tools to an AI application meant writing the same glue code over and over. You had OpenAI’s function calling syntax. Anthropic had tool use with a slightly different schema. LangChain abstracted over both, but now you depended on LangChain’s versioning decisions. Every new model provider meant rewriting your tool definitions. Every new tool meant re-registering it across every AI integration you maintained.

Stand up a Python MCP server and consume it from Python and .NET MAF agents, including discovery and AITool wiring from one server.

Revised with .NET examples — A newer version of this article, covering both Python and .NET, is available as part of the MAF v1: Python and .NET series: MAF v1 — 08-mcp-tools.

I was at Microsoft Ignite for the Foundry announcements, and the framing runs ahead of the substance. Microsoft rebranded most of its Azure AI stack as Microsoft Foundry: one PaaS spanning models, agents, tools, knowledge, and governance. Underneath the launch-day gloss, three things actually matter to anyone shipping enterprise agents.

Docker shipped a set of tools that let a platform team decide exactly which Model Context Protocol servers developers can run: a catalog format, an open-source Gateway, and a Toolkit built into Docker Desktop. The official catalog carries 220+ containerized MCP servers, and you can fork it, trim it, or build a private catalog that points at images in your own registry.