Build AI Agents using MCP

IBM follows the connection between an agent and the capabilities it can actually use. Three modules move from protocol architecture to server implementation and then client behavior, with labs for exposing tools, resources and prompts. The provider estimates a little over ten hours, making this a more substantial option than a brief protocol overview.

What you will learn

• Implement a FastMCP server with a defined set of capabilities.
• Connect clients through local STDIO or remote Streamable HTTP.
• Examine sampling, filesystem roots and user approval in agent workflows.

Who this suits

A practical next step for Python developers who understand how language models call tools and need to implement their own integration.

Before you enroll

IBM recommends Python and familiarity with LLM tools and data. Review the transport lessons against the current protocol documentation, particularly where older SSE examples are discussed.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Tool subscriptions, model API usage and cloud services may cost extra.

Put it into practice

Build a read-only document lookup service. Connect two clients and record how each handles invalid arguments, unavailable resources and a declined tool request.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

How YellowCourse describes and checks learning resources.