[UNIT] Unit 3.1 on building a PR bot with

units/en/_toctree.yml

@@ -43,12 +43,21 @@
   - local: unit2/tiny-agents
     title: Building Tiny Agents with MCP and the Hugging Face Hub
 
-- title: "3. Use Case: Advanced MCP Development"
+- title: "3.1. Use Case: Build a Pull Request Agent on the Hub"
   sections:
-  - local: unit3/introduction
-    title: Coming Soon
-
-- title: "Bonus Units"
-  sections:
-  - local: unit4/introduction
-    title: Coming Soon
+  - local: unit3_1/introduction
+    title: Build a Pull Request Agent on the Hugging Face Hub
+  - local: unit3_1/setting-up-the-project
+    title: Setting up the Project
+  - local: unit3_1/creating-the-mcp-server
+    title: Creating the MCP Server
+  - local: unit3_1/quiz1
+    title: Quiz 1 - MCP Server Implementation
+  - local: unit3_1/mcp-client
+    title: MCP Client
+  - local: unit3_1/webhook-listener
+    title: Webhook Listener
+  - local: unit3_1/quiz2
+    title: Quiz 2 - Pull Request Agent Integration
+  - local: unit3_1/conclusion
+    title: Conclusion

units/en/unit3_1/conclusion.mdx

@@ -0,0 +1,42 @@
+# Conclusion
+
+Congratulations! 🎉 You've successfully built a Pull Request Agent that automatically enhances Hugging Face model repositories through intelligent tagging using MCP (Model Context Protocol).
+
+The patterns you've learned - webhook processing, MCP tool integration, agent orchestration, and production deployment - are foundational skills for agent and MCP building. These techniques are applicable far beyond model tagging and represent a powerful approach to building intelligent systems that augment human capabilities.
+
+## What we've built
+
+Throughout this unit, you created a complete automation system with four key components:
+
+- **MCP Server** (`mcp_server.py`) - FastMCP-based server with Hub API integration
+- **MCP Client** (Agent) - Intelligent orchestration with language model reasoning  
+- **Webhook Listener** (FastAPI) - Real-time event processing from Hugging Face Hub
+- **Testing Interface** (Gradio) - Development and monitoring dashboard
+
+## Next Steps
+
+### Continue Learning
+- Explore advanced MCP patterns and tools
+- Study other automation frameworks and AI system architecture
+- Learn about multi-agent systems and tool composition
+
+### Build More Agents
+- Develop domain-specific automation tools for your own projects
+- Try out other types of webhooks (e.g. model uploads, model downloads, etc.)
+- Experiment with different workflows
+
+### Share Your Work
+- Open source your agent for the community
+- Write about your learnings and automation patterns
+- Contribute to the MCP ecosystem
+
+### Scale Your Impact
+- Deploy agents for multiple repositories or organizations
+- Build more sophisticated automation workflows
+- Explore commercial applications of AI automation
+
+<Tip>
+
+Consider documenting your experience and sharing it with the community! Your journey from learning MCP to building a production agent will help others explore AI automation.
+
+</Tip>