Extending Large Language Models (LLMs) with custom tools is increasingly valuable, and Model Context Protocol (MCP) servers offer a standardized way to enhance LLM capabilities by connecting them to external tools and resources. This tutorial guides users in building a document parsing server using FastMCP in Python, which enables MCP hosts to understand various file formats, extract text, and save content to local storage. The process involves setting up a development environment, creating a virtual environment, and managing dependencies with uv, a modern package manager. The tutorial also covers structuring a Python project according to recommended standards, implementing tools, resources, and prompts for the MCP server, and testing using pytest and MCP Inspector. Additionally, it details packaging the application for distribution, publishing on PyPI, and automating the process with CircleCI, ensuring a robust CI/CD workflow that handles testing, building, and publishing. The approach allows developers to tailor LLM capabilities to specific workflows, enhancing AI integration into various applications.