ServerDevelopment LLM Tool Integration Task Manager

MCP Task Manager (Python) 📌 Overview

This project is a Model Context Protocol (MCP) Server built in Python that exposes a simple Task Management system to any MCP-compatible AI client.

The goal of this project is to demonstrate how to:

Expose local functionality to LLMs in a structured way Enable AI systems to interact with real data and actions Build modular AI infrastructure using MCP 🚀 Why This Project?

Large Language Models (LLMs) are powerful but cannot directly access local systems, databases, or APIs.

To solve this:

MCP acts as a standard interface layer Instead of writing custom integrations for each AI tool, we build one MCP server Any MCP-compatible client (like Claude Desktop, Cursor, or custom agents) can use it

👉 This project simulates a real-world AI assistant that can:

Add tasks Track pending work Help organize workflows 🏗️ Architecture 🔹 Components MCP Host The application the user interacts with (e.g., Claude Desktop) MCP Client Lives inside the host Converts LLM requests into structured tool calls MCP Server (This Project) Exposes tools and data Handles execution of logic 🔄 Flow User gives instruction → “Add a task” LLM interprets intent MCP Client converts it → tool call MCP Server executes function Response is returned → shown to user 🧩 Features Implemented ✅ Tools (Core of MCP)

1. add_task Adds a new task to the system Inputs: title (required) description (optional) Output: Confirmation message
2. get_pending_tasks Retrieves all tasks with status = pending Output: Formatted list of tasks ⚙️ Tech Stack Component Tool Why Used MCP Framework FastMCP Simplifies JSON-RPC + tool exposure Language Python Rapid development + strong ecosystem Transport STDIO Avoids network overhead, ideal for local agents Storage In-memory list Simplicity for prototype 🧠 Why These Design Choices?
3. FastMCP Handles: Tool registration Schema generation Communication layer Avoids writing low-level protocol code

👉 Without this, you'd manually handle:

JSON-RPC parsing Tool metadata Request routing 2. In-Memory Storage Chosen for simplicity and demonstration No external dependencies

👉 Trade-off:

Data is lost when server stops

👉 Real-world alternative:

SQLite / PostgreSQL 3. Docstrings for Tools LLM reads docstrings to understand: What the function does When to use it

👉 Poor docstrings = wrong tool usage

4. STDIO Transport Communication via input/output streams No HTTP server required

👉 Benefits: No port conflicts

No firewall issues Works seamlessly with local AI tools