Overview

The My Practice MCP server is a lightweight, FastAPI‑based user management system that demonstrates how an AI assistant can interact with external services through a clean, well‑structured API. It solves the common problem of needing a simple yet robust CRUD backend for user data while keeping persistence to an easily editable CSV file. This design allows developers to prototype or integrate a user registry without the overhead of a full database, making it ideal for testing AI‑driven workflows or educational purposes.

At its core, the server exposes a set of REST endpoints that perform standard user operations: creating, deleting, listing, and bulk importing users from CSV. In addition, it offers a statistical endpoint that calculates the average age of users grouped by the first character of their name. Each operation is implemented in a Clean Architecture style, separating domain logic, use‑case orchestration, and infrastructure concerns. This separation ensures that the business rules remain independent of storage details, which is particularly valuable when an AI assistant needs to reason about data without being coupled to a specific persistence mechanism.

Key capabilities include:

• CSV‑based storage: Users are persisted in a simple CSV file, enabling quick setup and human‑readable data manipulation.
• Validation & error handling: Pydantic models enforce required fields, while custom exceptions provide clear, structured JSON responses for all failure modes (validation errors, missing users, malformed CSVs).
• Batch import: The endpoint allows bulk ingestion, which can be triggered by an AI assistant to populate the system from external datasets.
• Statistical analysis: The endpoint demonstrates how the server can perform data analytics, a feature often requested by AI assistants for generating insights.

In real‑world scenarios, this MCP server can serve as a backend for chatbots that manage contacts, help maintain membership lists, or support data‑driven decision making. An AI assistant can call the API to create a new user, validate inputs, or retrieve aggregated statistics—all while remaining agnostic of the underlying CSV implementation. The clear error contracts also make it straightforward for an assistant to interpret failures and guide users toward corrective actions.

Because the server follows Clean Architecture, developers can easily swap out the CSV repository for a database or an in‑memory store without touching the API layer. This flexibility, combined with the straightforward FastAPI interface and comprehensive Swagger documentation, makes My Practice a practical example of how MCP servers can bridge AI assistants with real‑world data services.