Conclusion

In this lesson, you successfully bridged the gap between your local code and the wider world of AI integration.

• You built a Custom MCP Client, proving that you don’t need a heavy desktop application to interact with MCP servers. You learned how to discover capabilities and execute tools purely through code.
• You created a Unified Server that served Tools, Resources, and Prompts simultaneously, simulating a complex, real-world application backend.
• You deployed an HTTP Server. By using streamable-http and tunneling via ngrok, you opened a secure channel for the cloud to reach your machine.
• Finally, you implemented the Anthropic MCP Connector. You built a script where the LLM acted as the brain—analyzing a prompt, realizing it needed help, and autonomously reaching out to your local server to perform a calculation.

You now have the architecture for a fully autonomous agent. You are no longer just chatting with a bot; you are building systems where the AI acts as an intelligent router, using your custom code to solve problems defined by the user.