Mcp Utils

Overview

is a lightweight, synchronous toolkit that simplifies the creation of Model Context Protocol (MCP) servers in Python. By abstracting away low‑level protocol details, it lets developers embed MCP capabilities directly into existing Flask applications or other WSGI‑compatible frameworks without the need for asynchronous programming. This focus on synchronous operation is particularly valuable in environments where legacy codebases or infrastructure constraints make async code difficult to adopt.

The package supplies a core class that handles message routing, schema validation, and response generation. Developers can register prompts and tools using clear decorators ( and ) that automatically convert Python functions into MCP‑compliant endpoints. Internally, the server leverages Pydantic models to enforce the MCP schema, ensuring that all requests and responses are correctly typed and documented.

Key features include Server‑Sent Events (SSE) support for streaming responses, a Redis‑backed response queue for decoupling request handling from downstream processing, and built‑in validation that catches schema violations before they reach the client. The library also ships with comprehensive documentation generation, allowing developers to expose an interactive API spec that MCP clients can consume.

In practice, is ideal for building custom tool integrations or prompt generators that can be queried by AI assistants such as Claude. A common use case is a weather‑lookup tool: developers expose a simple function, and the MCP server translates incoming JSON requests into that function call while returning the result in a format the assistant can understand. Because the server runs synchronously, it integrates smoothly with traditional database transactions (e.g., SQLAlchemy) and can be deployed behind a WSGI server like Gunicorn for production workloads.

Overall, offers a straightforward path to MCP compliance: developers write ordinary Python functions, annotate them with decorators, and let the library handle protocol details. This reduces boilerplate, improves type safety, and accelerates time‑to‑market for AI‑enabled services.