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I Made MCP AI Agents That Automate Every App I Build.

The Future of Apps: Conversational AI Control with FastAPI MCP Server

Remember the days of clicking through endless menus and dragging sliders to interact with software? That era is rapidly fading away. The future of applications is conversational—where a single dialogue with an AI agent controls everything. This revolution doesn't just apply to big tech apps; it extends to every application you build, transforming traditional interfaces into seamless, voice-driven experiences.

In this blog post, we’ll explore how the FastAPI MCP server is making this future a reality today. We’ll walk through how to integrate it with your app, enabling AI agents to fully control your frontend and backend via conversation.

What is FastAPI MCP Server?

FastAPI MCP server acts as a bridge between your app’s API and AI agents. It pulls your app’s APIs and exposes them on an MCP (Modular Conversational Pipeline) layer, turning each API endpoint into a callable tool that large language models (LLMs) can use on command.

This means no more buttons or dashboards—just natural language interaction to control your app.

Building a Conversational To-Do List App: Step-by-Step

To illustrate this, let’s build a simple to-do list app controlled entirely by an AI agent using FastAPI MCP server.

1. Setup Your Project Environment

  • Create a new project folder and open it in Cursor (the AI coding environment).
  • Create a Python virtual environment by selecting the interpreter when prompted.
  • This sets the stage for building both frontend and backend components.

2. Build the Frontend with Next.js

  • Use Cursor’s AI prompts to generate a Next.js frontend for the to-do list.
  • The frontend exposes the necessary APIs but does not handle backend logic yet.

3. Add the FastAPI Backend

  • Prompt Cursor to create FastAPI backend files, including models and endpoints for managing to-dos.
  • Now you have a fully functional to-do list app with both frontend and backend working together.

Integrating FastAPI MCP Server

4. Install the FastAPI MCP Package

  • Visit the FastAPI MCP GitHub repo (link in description).
  • Inside your virtual environment, install the package using pip:

bash pip install fastapi-mcp

5. Add MCP Server to Your FastAPI App

  • Copy the basic example code from the GitHub repo.
  • Paste it into your main FastAPI file.
  • This enables your API endpoints to be exposed as MCP tools that LLMs can call.

6. Naming Your API Tools

  • Use the operation_id tag in your FastAPI endpoints to assign meaningful names to each tool.
  • This helps AI agents know exactly which tool to call for specific actions.
  • If you skip this, tool names are autogenerated but less descriptive.

7. Register Your Tools Properly

  • Ensure the MCP server setup happens after all endpoints are declared.
  • If not, call the re-registration function at the end of your main file to make tools visible.

Connecting MCP Server to AI Clients

8. Link Your MCP Server with AI Agents

  • Run your FastAPI backend; it will provide a local URL (e.g., http://localhost:8000).
  • Add this URL as a global MCP server in your MCP client (Cursor, Claude, Windsurf, etc.) by editing the MCP.json file.
  • Append /mcp to the URL to point to the MCP server endpoint.

9. Control Your App via AI Agent

  • With everything connected, you can now control your app by simply talking to the AI agent.
  • For example, instruct the agent to add a task, and watch it appear instantly in your to-do app.
  • You can also break down complex tasks into steps, letting the AI agent manage the workflow.

Beyond Cursor: Building Fully Agentic AI Applications

While MCP clients like Cursor and Windsurf are great for demos and development, the real power lies in building fully autonomous AI applications.

The MCPUs framework enables you to:

  • Integrate MCP servers directly with AI agents.
  • Write code that lets agents interact with MCP tools automatically.
  • Build apps where AI agents handle everything—from UI to backend logic—just through conversation.

Imagine social media apps like Instagram or WhatsApp controlled entirely by AI agents responding to your natural language commands.

Final Thoughts

The FastAPI MCP server marks a significant shift in how we build and interact with applications. By exposing your API endpoints as tools accessible to AI agents, you unlock the potential for fully conversational, AI-driven app control without complicated UI development.

Whether you're building simple to-do apps or complex AI-powered platforms, integrating MCP servers can make your applications smarter and more intuitive.

Try It Yourself!

  • Check out the FastAPI MCP GitHub repo for installation and detailed documentation.
  • Experiment with creating your own conversational apps.
  • Explore the MCPUs framework to build fully autonomous AI agents.

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Thanks for reading! Feel free to ask questions or share your experiences using FastAPI MCP server in the comments below.

This blog post was inspired by a detailed walkthrough on building AI-controlled apps using FastAPI MCP server and AI agents.

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