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Functions, Tools and Agents with LangChainvsMCP: Build Rich-Context AI Apps with Anthropic

Side-by-side comparison on level, duration, pricing, instructor, tier. Editor verdict on which course wins for which buyer.

DL.AI
DeepLearning.AI

Functions, Tools and Agents with LangChain

The middle-of-the-curriculum short course bridging from prompt patterns to full agent loops. Covers OpenAI function calling, LangChain tools, OutputParsers, and the conversational-agent loop. Free, 90 minutes, taught by Harrison Chase. Take this after the LangChain for LLM App Development short course and before the LangGraph one — they form the canonical 3-course LangChain sequence.

DL.AI
DeepLearning.AI

MCP: Build Rich-Context AI Apps with Anthropic

MCP (Model Context Protocol) is the standard Anthropic introduced for connecting LLMs to external tools and data sources — and in 2026 it's becoming the lingua franca across Claude, Cursor, and most agent runtimes. This course is the canonical introduction, taught by Anthropic. Free, 90 minutes, hands-on building MCP servers and clients. The right course to take after the basic prompt engineering tutorials, before building production agents.

Side-by-side

DimensionFunctions, Tools and Agents with LangChainMCP: Build Rich-Context AI Apps with Anthropic
ProviderDeepLearning.AIDeepLearning.AI
Editorial tierHands-on reviewedHands-on reviewed
LevelIntermediateIntermediate
Formatself pacedself paced
Duration~1.5 hours (5 lessons)~1.5 hours
PricingFreeFree
InstructorHarrison Chase — Founder, LangChainElie Schoppik — Anthropic Developer Education
RatingNo public ratingNo public rating
Topicslangchain, build ai agents, prompt engineeringmcp, build ai agents
Last verified2026-05-242026-05-23

Pros & cons

Functions, Tools and Agents with LangChain
Pros
  • +By LangChain's founder — most authoritative source possible
  • +Bridges the gap between API-level prompting and full agentic loops
  • +Free, 90 minutes, fits in a single sitting
Cons
  • −Assumes prior LangChain basics — not a starting point
  • −Function-calling examples are OpenAI-flavored; patterns transfer to Anthropic
MCP: Build Rich-Context AI Apps with Anthropic
Pros
  • +Authored by Anthropic — MCP is their protocol
  • +Free, hands-on, fast
  • +MCP is becoming standard — investment compounds across Claude, Cursor, agent frameworks
Cons
  • −Newer course — feedback corpus is smaller than the older shorts
  • −Pre-req: comfortable with Python + API calls

Which course is for whom?

Functions, Tools and Agents with LangChain
Best for
  • · Engineers shipping their first LLM-with-tools app
  • · Anyone in the middle of the LangChain learning path
Not ideal for
  • · Complete beginners — start with LangChain for LLM App Development first
MCP: Build Rich-Context AI Apps with Anthropic
Best for
  • · Developers building production AI agents in 2026
  • · Anyone integrating LLMs with proprietary tools/data
Not ideal for
  • · Beginners — assumes Python + LLM basics
  • · Non-developers — MCP is fundamentally a developer-protocol

Editor's short verdict

These cover different primary topics — Functions, Tools and Agents with LangChain focuses on langchain while MCP: Build Rich-Context AI Apps with Anthropic focuses on mcp. Take the one matching your current goal first; the other can come later if your interests expand.

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Functions, Tools and Agents with LangChain vs MCP: Build Rich-Context AI Apps with Anthropic (2026): which course wins? · AI Agent Rank