Mcp

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Revised with .NET examples — A newer version of this article, covering both Python and .NET, is available as part of the MAF v1: Python and .NET series: MAF v1 — 08-mcp-tools.

Overview # I just returned from Microsoft Ignite in San Francisco, and if you’re building AI agents for enterprise, this was the event you needed to attend.

Docker has introduced comprehensive MCP (Model Context Protocol) tooling that enables organizations to build custom catalogs with complete control over AI tool access. With over 220+ containerized MCP servers available and the ability to create private catalogs, enterprises can now deploy AI tooling with appropriate security guardrails.