At a Glance

Name: Build an MCP Server and LangGraph Agent for Your Writing
Price: Free CAD
Rating: 0.0 (0 reviews)
Author: zikai_dou, tenzin_migmar

Modern AI agents reason about which tools to call and when. In this project, you'll build a Model Context Protocol (MCP) server exposing custom Python tools, then connect it to a LangGraph ReAct agent powered by OpenAI's gpt-5 to create a transparent, tool-using AI writing assistant. You'll learn MCP, the open standard rapidly becoming the universal language for connecting LLMs to tools, alongside LangGraph's agent orchestration patterns. Walk away ready to build agentic AI systems with custom tools for any domain.

Generic AI writing assistants are black boxes — paste text in, get a rewrite, hope for the best. The next generation of AI agents work differently: they use real, inspectable tools and reason about which one to call for which problem. This guided project teaches you to build exactly that, using two of the most important emerging technologies in applied AI: the Model Context Protocol (MCP) and LangGraph. You'll write a FastMCP server that exposes Python functions as tools, then connect it to a LangGraph ReAct agent that can autonomously discover and call those tools to deliver concrete, evidence-backed writing feedback.

What You'll Learn

By the end of this project, you will be able to:

Build custom MCP servers with FastMCP: Use @mcp.tool() decorators to turn ordinary Python functions into AI-callable tools that any MCP-compatible client (Claude Desktop, Cursor, LangGraph, and more) can discover and use — the same protocol powering production AI tool ecosystems.
Orchestrate tool-using agents with LangGraph: Use create_react_agent to build a ReAct-style agent that reasons about which tool to call, interprets the results, and loops until it has enough information to respond — the same pattern behind production AI agents.
Bridge MCP servers and LangGraph with langchain-mcp-adapters: Connect a LangGraph agent to a running MCP server via stdio transport, auto-discover tools, and wire them into the agent's tool list without writing custom integration code for each tool.

Who Should Enroll

Python developers who have used LLM APIs for single-turn prompts and want hands-on experience building multi-step agent systems with real tools.

AI engineers and ML practitioners curious about MCP — the open standard rapidly becoming the default way to connect AI applications to external tools and data sources.
Software engineers exploring LangGraph who want to learn the ReAct agent pattern and tool-calling workflow before adopting these patterns in personal projects.

Why Enroll

MCP and LangGraph represent two of the most important shifts in applied AI: a standard protocol for AI tool integration (MCP) and a framework for building stateful, multi-step agents (LangGraph). This project gives you hands-on experience with both — you'll finish with a working writing agent, a reusable MCP server you can plug into Claude Desktop or any other MCP client, and the architectural intuition to design transparent, tool-driven AI systems for any domain.

What You'll Need

You should be comfortable with Python and have basic familiarity with APIs (making HTTP requests, using API keys). Prior experience with LangChain or agent frameworks is not required — the project covers everything from the ground up. All dependencies are pre-configured in the environment, and the project runs best on current versions of Chrome, Edge, Firefox, or Safari.

Offered By: IBMSkillsNetwork

Build an MCP Server and LangGraph Agent for Your Writing

At a Glance

What You'll Learn

Who Should Enroll

Why Enroll

What You'll Need